CMIP6 prep
Camille
2024-02-19
library(raster)
library(plyr)
library(dplyr)
library(ggplot2)
library(viridisLite)
library(colorspace)
library(RColorBrewer)
library(sf)
library(stringr)
library(ggpubr)
library(kableExtra)
memory.limit(size = 160000)
## [1] Inf
#display.brewer.pal(n = 11, name ="RdYlGn")
# colorvec <- brewer.pal(n = 11, name ="RdYlGn")
# col.cat <- c("Hyperarid" = colorvec[2], "Arid"= colorvec[4],"Semi-arid"= colorvec[5], "Dry subhumid" = colorvec[8], "Humid"= colorvec[10], "Cold" = "powderblue")
#handmade
col.cat <- c("Hyperarid" = "#D73027", "Arid"= "#FDAE61","Semi-arid"= "#FEE08B", "Dry subhumid" = "#ABD9E9", "Humid"= "#4575B4", "Cold" = "#DAE2F7")
breaks.unesco <- c(-Inf,0,0.03,0.2,0.5,0.65,Inf)
cat.unesco <- c("(0,0.03]"= "Hyperarid", "(0.03,0.2]" = "Arid", "(0.2,0.5]" = "Semi-arid", "(0.5,0.65]" = "Dry subhumid","(0.65, Inf]" = "Humid", "(-Inf,0]" = "Cold")1 Data
Land-sea mask and IPCC region are taken from Iturbide et al. 2020: https://github.com/SantanderMetGroup/ATLAS/tree/devel/reference-grids
Elevation data is taken from Worldclim: https://www.worldclim.org/data/worldclim21.html
land_mask <- raster("Masks/land_sea_mask_1degree.nc4")
land_mask.df <- as.data.frame(land_mask, xy = T) %>% setNames(c("lon","lat","lm")) # 0 if ocean, 1 if land
elev <- raster("Worldclim/wc2.1_10m/wc2.1_10m_elev.tif")
elev.df <- elev %>% projectRaster(to = land_mask) %>% as.data.frame(xy = T) %>% setNames(c("lon","lat", "z"))
ipcc_regions <- shapefile("Masks/IPCC-WGI-reference-regions-v4.shp") %>% spTransform(crs("EPSG:4326"))
ipcc_regions.raster <- ipcc_regions %>% rasterize(land_mask)
ipcc_regions.df <- as.data.frame(ipcc_regions.raster, xy = T) %>% setNames(c("lon","lat","Continent","Type","Name","Acronym"))CMIP6 data is dowloaded from https://aims2.llnl.gov/search
The data was downloaded by model and extracted by model, period and SSP with the “data-prep-xxx.R” scripts, then grouped by variable (“bind-tables_xxx.R”), on GRICAD.
tas.all_annual <- read.table("CMIP6/tas/tas.all_annual.txt")
pr.all_annual <- read.table("CMIP6/pr/pr.all_annual.txt")
sfcWind.all_annual <- read.table("CMIP6/sfcWind/sfcWind.all_annual.txt")
hfls.all_annual <- read.table("CMIP6/hfls/hfls.all_annual.txt")
hfss.all_annual <- read.table("CMIP6/hfss/hfss.all_annual.txt")
hurs.all_annual <- read.table("CMIP6/hurs/hurs.all_annual.txt")
cmip6_annual <- tas.all_annual %>% merge(pr.all_annual, by = c("lon","lat","model","period","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_annual, by = c("lon","lat","model","period","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_annual, by = c("lon","lat","model","period","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_annual, by = c("lon","lat","model","period","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_annual, by = c("lon","lat","model","period","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_annual, "cmip6_annual.txt")tas.all_january <- read.table("CMIP6/tas/tas.all_january.txt")
pr.all_january <- read.table("CMIP6/pr/pr.all_january.txt")
sfcWind.all_january <- read.table("CMIP6/sfcWind/sfcWind.all_january.txt")
hfls.all_january <- read.table("CMIP6/hfls/hfls.all_january.txt")
hfss.all_january <- read.table("CMIP6/hfss/hfss.all_january.txt")
hurs.all_january <- read.table("CMIP6/hurs/hurs.all_january.txt")
cmip6_january <- tas.all_january %>% merge(pr.all_january, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_january, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_january, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_january, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_january, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_january, "cmip6_january.txt")tas.all_february <- read.table("CMIP6/tas/tas.all_february.txt")
pr.all_february <- read.table("CMIP6/pr/pr.all_february.txt")
sfcWind.all_february <- read.table("CMIP6/sfcWind/sfcWind.all_february.txt")
hfls.all_february <- read.table("CMIP6/hfls/hfls.all_february.txt")
hfss.all_february <- read.table("CMIP6/hfss/hfss.all_february.txt")
hurs.all_february <- read.table("CMIP6/hurs/hurs.all_february.txt")
cmip6_february <- tas.all_february %>% merge(pr.all_february, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_february, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_february, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_february, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_february, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_february, "cmip6_february.txt")tas.all_march <- read.table("CMIP6/tas/tas.all_march.txt")
pr.all_march <- read.table("CMIP6/pr/pr.all_march.txt")
sfcWind.all_march <- read.table("CMIP6/sfcWind/sfcWind.all_march.txt")
hfls.all_march <- read.table("CMIP6/hfls/hfls.all_march.txt")
hfss.all_march <- read.table("CMIP6/hfss/hfss.all_march.txt")
hurs.all_march <- read.table("CMIP6/hurs/hurs.all_march.txt")
cmip6_march <- tas.all_march %>% merge(pr.all_march, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_march, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_march, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_march, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_march, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_march, "cmip6_march.txt")tas.all_april <- read.table("CMIP6/tas/tas.all_april.txt")
pr.all_april <- read.table("CMIP6/pr/pr.all_april.txt")
sfcWind.all_april <- read.table("CMIP6/sfcWind/sfcWind.all_april.txt")
hfls.all_april <- read.table("CMIP6/hfls/hfls.all_april.txt")
hfss.all_april <- read.table("CMIP6/hfss/hfss.all_april.txt")
hurs.all_april <- read.table("CMIP6/hurs/hurs.all_april.txt")
cmip6_april <- tas.all_april %>% merge(pr.all_april, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_april, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_april, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_april, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_april, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_april, "cmip6_april.txt")tas.all_may <- read.table("CMIP6/tas/tas.all_may.txt")
pr.all_may <- read.table("CMIP6/pr/pr.all_may.txt")
sfcWind.all_may <- read.table("CMIP6/sfcWind/sfcWind.all_may.txt")
hfls.all_may <- read.table("CMIP6/hfls/hfls.all_may.txt")
hfss.all_may <- read.table("CMIP6/hfss/hfss.all_may.txt")
hurs.all_may <- read.table("CMIP6/hurs/hurs.all_may.txt")
cmip6_may <- tas.all_may %>% merge(pr.all_may, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_may, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_may, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_may, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_may, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_may, "cmip6_may.txt")tas.all_june <- read.table("CMIP6/tas/tas.all_june.txt")
pr.all_june <- read.table("CMIP6/pr/pr.all_june.txt")
sfcWind.all_june <- read.table("CMIP6/sfcWind/sfcWind.all_june.txt")
hfls.all_june <- read.table("CMIP6/hfls/hfls.all_june.txt")
hfss.all_june <- read.table("CMIP6/hfss/hfss.all_june.txt")
hurs.all_june <- read.table("CMIP6/hurs/hurs.all_june.txt")
cmip6_june <- tas.all_june %>% merge(pr.all_june, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_june, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_june, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_june, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_june, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_june, "cmip6_june.txt")tas.all_july <- read.table("CMIP6/tas/tas.all_july.txt")
pr.all_july <- read.table("CMIP6/pr/pr.all_july.txt")
sfcWind.all_july <- read.table("CMIP6/sfcWind/sfcWind.all_july.txt")
hfls.all_july <- read.table("CMIP6/hfls/hfls.all_july.txt")
hfss.all_july <- read.table("CMIP6/hfss/hfss.all_july.txt")
hurs.all_july <- read.table("CMIP6/hurs/hurs.all_july.txt")
cmip6_july <- tas.all_july %>% merge(pr.all_july, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_july, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_july, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_july, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_july, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_july, "cmip6_july.txt")tas.all_august <- read.table("CMIP6/tas/tas.all_august.txt")
pr.all_august <- read.table("CMIP6/pr/pr.all_august.txt")
sfcWind.all_august <- read.table("CMIP6/sfcWind/sfcWind.all_august.txt")
hfls.all_august <- read.table("CMIP6/hfls/hfls.all_august.txt")
hfss.all_august <- read.table("CMIP6/hfss/hfss.all_august.txt")
hurs.all_august <- read.table("CMIP6/hurs/hurs.all_august.txt")
cmip6_august <- tas.all_august %>% merge(pr.all_august, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_august, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_august, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_august, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_august, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_august, "cmip6_august.txt")tas.all_september <- read.table("CMIP6/tas/tas.all_september.txt")
pr.all_september <- read.table("CMIP6/pr/pr.all_september.txt")
sfcWind.all_september <- read.table("CMIP6/sfcWind/sfcWind.all_september.txt")
hfls.all_september <- read.table("CMIP6/hfls/hfls.all_september.txt")
hfss.all_september <- read.table("CMIP6/hfss/hfss.all_september.txt")
hurs.all_september <- read.table("CMIP6/hurs/hurs.all_september.txt")
cmip6_september <- tas.all_september %>% merge(pr.all_september, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_september, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_september, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_september, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_september, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_september, "cmip6_september.txt")tas.all_october <- read.table("CMIP6/tas/tas.all_october.txt")
pr.all_october <- read.table("CMIP6/pr/pr.all_october.txt")
sfcWind.all_october <- read.table("CMIP6/sfcWind/sfcWind.all_october.txt")
hfls.all_october <- read.table("CMIP6/hfls/hfls.all_october.txt")
hfss.all_october <- read.table("CMIP6/hfss/hfss.all_october.txt")
hurs.all_october <- read.table("CMIP6/hurs/hurs.all_october.txt")
cmip6_october <- tas.all_october %>% merge(pr.all_october, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_october, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_october, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_october, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_october, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_october, "cmip6_october.txt")tas.all_november <- read.table("CMIP6/tas/tas.all_november.txt")
pr.all_november <- read.table("CMIP6/pr/pr.all_november.txt")
sfcWind.all_november <- read.table("CMIP6/sfcWind/sfcWind.all_november.txt")
hfls.all_november <- read.table("CMIP6/hfls/hfls.all_november.txt")
hfss.all_november <- read.table("CMIP6/hfss/hfss.all_november.txt")
hurs.all_november <- read.table("CMIP6/hurs/hurs.all_november.txt")
cmip6_november <- tas.all_november %>% merge(pr.all_november, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_november, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_november, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_november, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_november, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_november, "cmip6_november.txt")tas.all_december <- read.table("CMIP6/tas/tas.all_december.txt")
pr.all_december <- read.table("CMIP6/pr/pr.all_december.txt")
sfcWind.all_december <- read.table("CMIP6/sfcWind/sfcWind.all_december.txt")
hfls.all_december <- read.table("CMIP6/hfls/hfls.all_december.txt")
hfss.all_december <- read.table("CMIP6/hfss/hfss.all_december.txt")
hurs.all_december <- read.table("CMIP6/hurs/hurs.all_december.txt")
cmip6_december <- tas.all_december %>% merge(pr.all_december, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(sfcWind.all_december, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfls.all_december, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hfss.all_december, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(hurs.all_december, by = c("lon","lat","model","period","month","lm", "Continent","Type","Name","Acronym","source")) %>%
merge(elev.df, by = c("lon", "lat"))
write.table(cmip6_december, "cmip6_december.txt")2 Aridity Index
2.1 De Martonne
cmip6 <- read.table("cmip6.ref.txt") # created in "figures_article.Rmd", already converted in mm and °C
breaks.martonne <- c(0,5,10,20,"30","40",Inf)
cat.martonne <- c("[0,5]" = "Desert", "(5,10]"= "Arid", "(10,20]" = "Semi-arid","(20,30]" = "Temperate", "(30,40]" = "Humid", "(40,Inf]" = "Forest")
col.martonne <- c("Desert" = "#D73027", "Arid"= "#FDAE61","Semi-arid"= "#FEE08B", "Temperate" = "#ABD9E9", "Humid"= "#4575B4","Forest"= "#DAE2F7")
cmip6$AIm <- with(cmip6,spr.ref/(t.ref)) # pr in mm/y, tas in Ceslsius
cmip6$cat.AIm <- cmip6$AIm %>% cut(breaks.martonne, include.lowest = T) %>% revalue(cat.martonne)
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "1970_2000" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AIm))+
scale_fill_manual(values = col.martonne, na.translate = F)+
labs(title = paste(i, "1970-2000", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
2.2 AI FAO (with Penman-Monteith)
ea from Rh:
ea = 6.108(RH/100)exp(17.27*T/(T+237.3))
Refs: https://www.nature.com/articles/s41598-023-40499-6 : Allen and Tetens
All variables are calculated by month, so that we know the monthly evapotranspiration. The monthly values are summed to obtain the annual values.
id_vars <- c("lon","lat","model","period","lm", "Continent","Type","Name","Acronym","source")
cmip6_annual <- mutate(read.table("cmip6_annual.txt"),
t = tas - 273.15,
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_annual = ((0.408*delta*Rn+gamma*900/((t)+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_01 <- mutate(read.table("cmip6_january.txt"),
t = tas - 273.15,
pr_01 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_01 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind)))) # Evapotranspiration, G considered to be negligible. in mm/day. Wind speed at 10 m is converted to wind speed at 2 m by multiplying by 0.748
cmip6_02 <- mutate(read.table("cmip6_february.txt"),
t = tas - 273.15,
pr_02 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_02 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_03 <- mutate(read.table("cmip6_march.txt"),
t = tas - 273.15,
pr_03 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_03 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_04 <- mutate(read.table("cmip6_april.txt"),
t = tas - 273.15,
pr_04 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_04 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_05 <- mutate(read.table("cmip6_may.txt"),
t = tas - 273.15,
pr_05 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_05 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_06 <- mutate(read.table("cmip6_june.txt"),
t = tas - 273.15,
pr_06 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_06 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_07 <- mutate(read.table("cmip6_july.txt"),
t = tas - 273.15,
pr_07 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_07 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_08 <- mutate(read.table("cmip6_august.txt"),
t = tas - 273.15,
pr_08 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_08 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_09 <- mutate(read.table("cmip6_september.txt"),
t = tas - 273.15,
pr_09 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_09 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_10 <- mutate(read.table("cmip6_october.txt"),
t = tas - 273.15,
pr_10 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_10 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_11 <- mutate(read.table("cmip6_november.txt"),
t = tas - 273.15,
pr_11 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_11 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
cmip6_12 <- mutate(read.table("cmip6_december.txt"),
t = tas - 273.15,
pr_12 = pr*60*60*24, # mm/day
Rn = (hfls + hfss) * 86400 * 1e-6, # conversion from W/m2 to MJ/m2/day.
P = 101.3 * ((293-0.0065*z)/293)^(5.26),
es = 0.61078*exp(17.27*t/(t+237.3)),
ea = (hurs/100)*es,
delta = 4098*es/(t+237.3)^2, #slope vapor pressure
gamma = 0.00163*P/(2.501-52.6361e-3*t), #psychrometric constant
ET0_12 = ((0.408*delta*Rn+gamma*900/(t+273)*0.748*sfcWind*(es-ea))/(delta+gamma*(1+0.34*0.748*sfcWind))))
et0_monthly <- select(cmip6_annual, id_vars) %>%
merge(select(cmip6_01, c(id_vars, "ET0_01", "pr_01")), by = id_vars) %>%
merge(select(cmip6_02, c(id_vars, "ET0_02", "pr_02")), by = id_vars) %>%
merge(select(cmip6_03, c(id_vars, "ET0_03", "pr_03")), by = id_vars) %>%
merge(select(cmip6_04, c(id_vars, "ET0_04", "pr_04")), by = id_vars) %>%
merge(select(cmip6_05, c(id_vars, "ET0_05", "pr_05")), by = id_vars) %>%
merge(select(cmip6_06, c(id_vars, "ET0_06", "pr_06")), by = id_vars) %>%
merge(select(cmip6_07, c(id_vars, "ET0_07", "pr_07")), by = id_vars) %>%
merge(select(cmip6_08, c(id_vars, "ET0_08", "pr_08")), by = id_vars) %>%
merge(select(cmip6_09, c(id_vars, "ET0_09", "pr_09")), by = id_vars) %>%
merge(select(cmip6_10, c(id_vars, "ET0_10", "pr_10")), by = id_vars) %>%
merge(select(cmip6_11, c(id_vars, "ET0_11", "pr_11")), by = id_vars) %>%
merge(select(cmip6_12, c(id_vars, "ET0_12", "pr_12")), by = id_vars)
et0_monthly$spr <- with(et0_monthly, pr_01*31 + pr_02 * 28 + pr_03 * 31 + pr_04 * 30 + pr_05 * 31 + pr_06 * 30 +
pr_07 * 31 + pr_08 * 31 + pr_09 * 30 + pr_10 * 31 + pr_11 * 30 + pr_12 * 31)
et0_monthly$sET0 <- with(et0_monthly, ET0_01*31 + ET0_02 * 28 + ET0_03 * 31 + ET0_04 * 30 + ET0_05 * 31 + ET0_06 * 30 +
ET0_07 * 31 + ET0_08 * 31 + ET0_09 * 30 + ET0_10 * 31 + ET0_11 * 30 + ET0_12 * 31)
cmip6 <- merge(cmip6_annual, et0_monthly, by = id_vars)
cmip6$AI <- with(cmip6, abs(spr/sET0))
cmip6$AI_annual <- with(cmip6, abs((pr*60*60*24*365)/(ET0_annual*365)))
breaks.unesco <- c(-Inf,0,0.03,0.2,0.5,0.65,Inf)
cat.unesco <- c("(0,0.03]"= "Hyperarid", "(0.03,0.2]" = "Arid", "(0.2,0.5]" = "Semi-arid", "(0.5,0.65]" = "Dry subhumid","(0.65, Inf]" = "Humid", "(-Inf,0]" = "Cold")
#col.cat <- c("Hyperarid" = colorvec[2], "Arid"= colorvec[4],"Semi-arid"= colorvec[5], "Dry subhumid" = colorvec[8], "Humid"= colorvec[10], "Cold" = "powderblue")
col.cat <- c("Hyperarid" = "#D73027", "Arid"= "#FDAE61","Semi-arid"= "#FEE08B", "Dry subhumid" = "#ABD9E9", "Humid"= "#4575B4", "Cold" = "#DAE2F7")
cmip6$cat.AI <- cmip6$AI %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6$cat.AI[which(cmip6$sET0 < 400)] <- "Cold"
cmip6$cat.AI %>% unique()
cmip6$cat.AI <- cmip6$AI %>% cut(breaks = breaks.unesco) %>%
revalue(cat.unesco)
cmip6$cat.AI_annual <- cmip6$AI_annual %>% cut(breaks = breaks.unesco) %>%
revalue(cat.unesco)
cmip6$cat.AI_annual[which(cmip6$ET0_annual*365 < 400)] <- "Cold"
write.table(cmip6, "cmip6_AI.txt")3 Maps AI by model, all % calculated without Antarctica
cmip6 <- read.table("cmip6_AI.txt")
breaks.unesco <- c(-Inf,0.03,0.2,0.5,0.65,Inf)
cat.unesco <- c("(-Inf,0.03]"= "Hyperarid", "(0.03,0.2]" = "Arid", "(0.2,0.5]" = "Semi-arid", "(0.5,0.65]" = "Dry subhumid","(0.65, Inf]" = "Humid")
cmip6$cat.AI <- factor(cmip6$cat.AI, levels = c("Hyperarid","Arid","Semi-arid","Dry subhumid", "Humid","Cold"))3.1 1850-1880
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "1850_1880" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "1850-1880", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "1850_1880" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "1850_1880" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T) | source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 5.1 | 9.0 | 13.2 | 6.1 | 33.4 | 38.0 | 28.3 | 0.3 |
| CESM | 5.2 | 10.8 | 14.7 | 5.0 | 35.7 | 35.6 | 28.5 | 0.3 |
| CMCC | 5.6 | 7.2 | 9.5 | 5.3 | 27.6 | 42.1 | 30.1 | 0.3 |
| CMCC-ESM2 | 6.0 | 8.2 | 7.5 | 2.8 | 24.5 | 24.0 | 51.2 | 0.3 |
| CNRM | 5.7 | 12.1 | 11.4 | 6.6 | 35.8 | 38.0 | 25.9 | 0.2 |
| EC-Earth3 | 8.6 | 9.0 | 11.0 | 4.5 | 33.1 | 36.7 | 30.1 | 0.2 |
| FGOALS | 5.6 | 12.8 | 15.9 | 7.2 | 41.5 | 29.9 | 28.4 | 0.2 |
| GFDL-ESM4 | 5.6 | 9.3 | 8.7 | 4.1 | 27.7 | 37.0 | 34.9 | 0.2 |
| INM | 4.4 | 8.2 | 13.4 | 5.8 | 31.8 | 40.6 | 27.4 | 0.3 |
| INM-CM5 | 3.5 | 8.8 | 11.5 | 5.6 | 29.4 | 43.2 | 27.1 | 0.3 |
| MPI | 8.5 | 10.9 | 10.1 | 4.4 | 33.9 | 34.5 | 31.4 | 0.2 |
| MRI | 6.3 | 10.6 | 8.7 | 3.8 | 29.4 | 39.6 | 30.9 | 0.3 |
| NorESM-2-MM | 5.1 | 10.4 | 18.4 | 5.9 | 39.8 | 35.4 | 24.4 | 0.3 |
| mean | 5.8 | 9.8 | 11.8 | 5.2 | 32.6 | 36.5 | 30.7 | 0.3 |
| sd | 1.4 | 1.6 | 3.2 | 1.2 | 7.4 | 5.1 | 6.7 | 0.1 |
3.2 1970-2000
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "1970_2000" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "1970-2000", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
library(ggrepel)
pie <- cmip6 %>% subset(period == "1970_2000" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
select(c("source", "cat.AI")) %>%
reshape2::melt(id.vars = c("source")) %>%
group_by(source, value) %>%
summarise(count = n()) %>%
mutate(perc = round(count/sum(count)*100, 1),
lab.y = rev(cumsum(rev(count))) - count*0.5) %>%
ungroup()
pie_list <- list()
for(i in unique(pie$source)){
p <- ggplot(subset(pie, source == i))+
geom_bar(aes(x = "", y = count, fill = value), width = 1, stat = "identity")+
coord_polar("y", start = 0)+
scale_fill_manual(values = col.cat)+
geom_label_repel(aes(x = "", y = lab.y, label = perc), nudge_x = 0.5)+
labs(fill = "", title = i)+
theme_void()+theme(legend.position = "right")
pie_list[[i]] <- p
}
ggpubr::ggarrange(plotlist = pie_list, nrow = 4, ncol = 4, common.legend = T, legend = "bottom")
pie <- cmip6 %>% subset(period == "1970_2000" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
select(c("cat.AI")) %>%
table() %>% as.data.frame() %>%
mutate(perc = round(Freq/sum(Freq)*100, 1),
lab.y = rev(cumsum(rev(Freq))) - Freq*0.5)
p <- ggplot(pie)+
geom_bar(aes(x = "", y = Freq, fill = cat.AI), width = 1, stat = "identity")+
coord_polar("y", start = 0)+
scale_fill_manual(values = col.cat)+
geom_label_repel(aes(x = "", y = lab.y, label = perc), nudge_x = 0.5)+
labs(fill = "", title = "Multimodel CMIP6")+
theme_void()+theme(legend.position = "right")
print(p)
sdmm <- cmip6 %>% subset(period == "1970_2000" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "1970_2000" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T) | source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 5.2 | 8.6 | 13.2 | 6.3 | 33.3 | 37.4 | 29.1 | 0.3 |
| CESM | 5.3 | 10.7 | 14.1 | 5.2 | 35.3 | 35.0 | 29.5 | 0.3 |
| CMCC | 5.1 | 7.4 | 10.1 | 5.4 | 28.0 | 42.2 | 29.5 | 0.3 |
| CMCC-ESM2 | 6.4 | 7.9 | 7.7 | 2.5 | 24.5 | 24.4 | 50.8 | 0.3 |
| CNRM | 5.8 | 12.2 | 11.3 | 6.5 | 35.8 | 38.3 | 25.7 | 0.2 |
| EC-Earth3 | 8.4 | 9.5 | 10.8 | 4.4 | 33.1 | 35.7 | 31.0 | 0.2 |
| FGOALS | 5.9 | 10.9 | 10.0 | 4.9 | 31.7 | 25.1 | 42.9 | 0.2 |
| GFDL-ESM4 | 5.8 | 8.9 | 8.9 | 4.1 | 27.7 | 35.3 | 36.8 | 0.2 |
| INM | 4.4 | 8.4 | 13.3 | 5.7 | 31.8 | 40.1 | 27.8 | 0.3 |
| INM-CM5 | 3.4 | 8.5 | 12.0 | 5.6 | 29.5 | 43.0 | 27.3 | 0.3 |
| MPI | 8.7 | 11.1 | 10.2 | 4.3 | 34.3 | 34.0 | 31.5 | 0.2 |
| MRI | 6.1 | 10.8 | 9.2 | 3.9 | 30.0 | 38.9 | 30.9 | 0.3 |
| NorESM-2-MM | 5.2 | 10.1 | 17.1 | 5.6 | 38.0 | 33.4 | 28.3 | 0.3 |
| mean | 5.8 | 9.6 | 11.4 | 5.0 | 31.8 | 35.6 | 32.4 | 0.3 |
| sd | 1.4 | 1.5 | 2.5 | 1.1 | 6.5 | 5.7 | 7.1 | 0.1 |
3.3 1985-2010
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "1985_2015" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "1985-2010", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "1985_2015" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "1985_2015" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T) | source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 5.3 | 8.4 | 13.8 | 6.6 | 34.1 | 37.2 | 28.3 | 0.3 |
| CESM | 4.8 | 10.8 | 14.8 | 5.4 | 35.8 | 35.4 | 28.5 | 0.3 |
| CMCC | 5.2 | 7.6 | 10.4 | 5.8 | 29.0 | 42.9 | 27.9 | 0.3 |
| CMCC-ESM2 | 6.2 | 8.5 | 7.7 | 2.5 | 24.9 | 24.5 | 50.3 | 0.3 |
| CNRM | 5.8 | 12.1 | 11.3 | 6.2 | 35.4 | 39.2 | 25.2 | 0.2 |
| EC-Earth3 | 8.3 | 9.8 | 10.7 | 4.4 | 33.2 | 36.7 | 29.9 | 0.2 |
| FGOALS | 5.8 | 11.4 | 10.4 | 4.9 | 32.5 | 25.3 | 42.0 | 0.2 |
| GFDL-ESM4 | 5.9 | 9.1 | 9.1 | 3.9 | 28.0 | 36.5 | 35.3 | 0.2 |
| INM | 4.0 | 7.7 | 11.3 | 5.2 | 28.2 | 36.5 | 35.0 | 0.3 |
| INM-CM5 | 3.5 | 8.3 | 12.0 | 5.4 | 29.2 | 40.6 | 29.9 | 0.3 |
| MPI | 8.7 | 11.1 | 10.3 | 4.4 | 34.5 | 34.1 | 31.0 | 0.2 |
| MRI | 6.2 | 10.7 | 9.1 | 3.8 | 29.8 | 38.4 | 31.6 | 0.3 |
| NorESM-2-MM | 4.7 | 9.9 | 15.9 | 5.6 | 36.1 | 30.3 | 33.2 | 0.3 |
| mean | 5.7 | 9.6 | 11.3 | 4.9 | 31.5 | 35.2 | 32.9 | 0.3 |
| sd | 1.5 | 1.5 | 2.3 | 1.1 | 6.4 | 5.5 | 6.7 | 0.1 |
3.4 SSP 2-4.5
3.4.1 2030-2060
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "2030_2060" & model == "SSP245" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "2030-2060", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "2030_2060" & model == "SSP245" &!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "2030_2060" & model == "SSP245" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T) | source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 5.0 | 9.2 | 14.6 | 6.5 | 35.3 | 38.1 | 26.2 | 0.3 |
| CESM | 4.7 | 12.4 | 15.3 | 5.6 | 38.0 | 35.6 | 26.2 | 0.3 |
| CMCC | 4.4 | 8.4 | 11.0 | 6.1 | 29.9 | 44.3 | 25.5 | 0.3 |
| CMCC-ESM2 | 6.1 | 9.1 | 8.0 | 2.8 | 26.0 | 25.3 | 48.4 | 0.3 |
| CNRM | 6.0 | 12.7 | 12.1 | 6.8 | 37.6 | 39.8 | 22.3 | 0.2 |
| EC-Earth3 | 7.5 | 9.2 | 11.1 | 4.5 | 32.3 | 36.3 | 31.2 | 0.2 |
| FGOALS | 6.9 | 13.3 | 18.5 | 7.8 | 46.5 | 30.3 | 22.9 | 0.2 |
| GFDL-ESM4 | 5.4 | 10.1 | 9.5 | 4.4 | 29.4 | 36.9 | 33.4 | 0.2 |
| INM | 4.6 | 8.4 | 13.8 | 5.9 | 32.7 | 40.4 | 26.7 | 0.3 |
| INM-CM5 | 3.5 | 8.6 | 12.2 | 5.4 | 29.7 | 43.1 | 26.9 | 0.3 |
| MPI | 9.0 | 11.9 | 11.1 | 4.5 | 36.5 | 35.1 | 28.2 | 0.2 |
| MRI | 6.2 | 11.2 | 9.3 | 4.1 | 30.8 | 40.0 | 28.9 | 0.3 |
| NorESM-2-MM | 5.3 | 11.8 | 16.9 | 5.6 | 39.6 | 33.5 | 26.7 | 0.3 |
| mean | 4.0 | 7.3 | 8.7 | 3.7 | 23.7 | 25.6 | 48.0 | 2.6 |
| sd | 1.0 | 1.2 | 2.2 | 0.9 | 5.3 | 3.6 | 4.6 | 0.0 |
3.4.2 2070-2100
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "2070_2100" & model == "SSP245" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "2070-2100", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "2070_2100" & model == "SSP245" &!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "2070_2100" & model == "SSP245" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T) | source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 4.8 | 10.5 | 15.0 | 7.3 | 37.6 | 38.2 | 24.1 | 0.3 |
| CESM | 6.1 | 11.6 | 15.4 | 5.4 | 38.5 | 36.9 | 24.3 | 0.3 |
| CMCC | 4.3 | 8.0 | 12.3 | 6.4 | 31.0 | 46.6 | 22.1 | 0.3 |
| CMCC-ESM2 | 6.2 | 9.5 | 8.5 | 2.8 | 27.0 | 26.8 | 46.0 | 0.3 |
| CNRM | 6.3 | 12.9 | 12.4 | 6.8 | 38.4 | 41.0 | 20.4 | 0.2 |
| EC-Earth3 | 7.6 | 9.1 | 11.0 | 4.4 | 32.1 | 37.3 | 30.3 | 0.2 |
| FGOALS | 6.9 | 14.0 | 18.1 | 8.1 | 47.1 | 30.3 | 22.3 | 0.2 |
| GFDL-ESM4 | 5.9 | 9.7 | 9.8 | 4.4 | 29.8 | 38.4 | 31.6 | 0.2 |
| INM | 4.8 | 8.2 | 14.1 | 5.8 | 32.9 | 40.6 | 26.2 | 0.3 |
| INM-CM5 | 3.7 | 9.5 | 11.2 | 5.9 | 30.3 | 43.5 | 25.9 | 0.3 |
| MPI | 9.0 | 11.9 | 11.1 | 4.5 | 36.5 | 35.1 | 28.2 | 0.2 |
| MRI | 6.5 | 11.0 | 9.6 | 4.0 | 31.1 | 40.8 | 27.9 | 0.3 |
| NorESM-2-MM | 5.9 | 11.7 | 17.0 | 5.9 | 40.5 | 34.0 | 25.3 | 0.3 |
| mean | 4.2 | 7.4 | 8.8 | 3.8 | 24.2 | 26.2 | 47.0 | 2.6 |
| sd | 1.0 | 1.3 | 2.1 | 1.0 | 5.4 | 3.7 | 4.5 | 0.0 |
3.5 SSP 3-7.0
3.5.1 2030-2060
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "2030_2060" & model == "SSP370" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "2030-2060", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "2030_2060" & model == "SSP370" &!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "2030_2060" & model == "SSP370" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T) | source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 4.9 | 9.6 | 14.3 | 6.8 | 35.6 | 37.4 | 26.8 | 0.3 |
| CESM | 5.2 | 11.4 | 15.6 | 5.4 | 37.6 | 35.7 | 26.5 | 0.3 |
| CMCC | 4.2 | 8.3 | 11.3 | 5.9 | 29.7 | 44.1 | 25.9 | 0.3 |
| CMCC-ESM2 | 3.3 | 8.4 | 9.0 | 3.6 | 24.3 | 26.9 | 48.6 | 0.3 |
| CNRM | 6.2 | 12.9 | 12.9 | 7.1 | 39.1 | 39.6 | 21.0 | 0.2 |
| EC-Earth3 | 7.9 | 9.7 | 11.6 | 4.8 | 34.0 | 38.5 | 27.3 | 0.2 |
| FGOALS | 6.1 | 11.9 | 11.9 | 5.3 | 35.2 | 27.5 | 37.0 | 0.2 |
| GFDL-ESM4 | 6.3 | 9.4 | 9.9 | 4.1 | 29.7 | 35.9 | 34.1 | 0.2 |
| INM | 4.6 | 8.3 | 13.5 | 5.9 | 32.3 | 40.3 | 27.1 | 0.3 |
| INM-CM5 | 3.4 | 8.8 | 12.1 | 5.6 | 29.9 | 43.2 | 26.5 | 0.3 |
| MPI | 8.7 | 12.2 | 11.0 | 4.5 | 36.4 | 36.4 | 26.9 | 0.2 |
| MRI | 6.5 | 10.8 | 9.4 | 3.6 | 30.3 | 40.2 | 29.2 | 0.3 |
| NorESM-2-MM | 5.4 | 11.0 | 17.9 | 5.7 | 40.0 | 33.2 | 26.5 | 0.3 |
| mean | 3.9 | 7.1 | 8.6 | 3.7 | 23.3 | 25.6 | 48.5 | 2.6 |
| sd | 1.1 | 1.1 | 1.8 | 0.8 | 4.8 | 3.6 | 4.8 | 0.0 |
3.5.2 2070-2100
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "2070_2100" & model == "SSP370" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "2070-2100", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "2070_2100" & model == "SSP370" &!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "2070_2100" & model == "SSP370" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T) | source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 5.5 | 10.5 | 15.4 | 6.9 | 38.3 | 37.3 | 24.2 | 0.3 |
| CESM | 6.0 | 12.2 | 15.6 | 5.7 | 39.5 | 37.4 | 22.9 | 0.3 |
| CMCC | 5.2 | 9.1 | 12.5 | 7.3 | 34.1 | 44.1 | 21.6 | 0.3 |
| CMCC-ESM2 | 6.9 | 9.9 | 8.1 | 3.0 | 27.9 | 25.9 | 45.9 | 0.3 |
| CNRM | 7.3 | 12.9 | 13.2 | 6.7 | 40.1 | 39.3 | 20.3 | 0.2 |
| EC-Earth3 | 7.6 | 9.8 | 12.4 | 4.7 | 34.5 | 40.3 | 24.9 | 0.2 |
| FGOALS | 6.7 | 13.3 | 13.3 | 4.4 | 37.7 | 28.0 | 34.1 | 0.2 |
| GFDL-ESM4 | 7.5 | 8.5 | 10.9 | 4.7 | 31.6 | 37.0 | 31.1 | 0.2 |
| INM | 6.0 | 8.2 | 14.7 | 6.1 | 35.0 | 39.2 | 25.5 | 0.3 |
| INM-CM5 | 3.9 | 10.3 | 14.1 | 6.1 | 34.4 | 40.4 | 24.9 | 0.3 |
| MPI | 8.7 | 12.2 | 11.0 | 4.5 | 36.4 | 36.4 | 26.9 | 0.2 |
| MRI | 7.2 | 11.9 | 9.9 | 4.1 | 33.1 | 39.0 | 27.6 | 0.3 |
| NorESM-2-MM | 5.9 | 11.8 | 17.5 | 5.9 | 41.1 | 33.7 | 24.9 | 0.3 |
| mean | 4.5 | 7.5 | 9.0 | 3.8 | 24.8 | 25.6 | 47.0 | 2.6 |
| sd | 0.9 | 1.2 | 1.8 | 0.9 | 4.8 | 3.5 | 4.7 | 0.0 |
3.6 SSP 5-8.5
3.6.1 2030-2060
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "2030_2060" & model == "SSP585" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "2030-2060", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "2030_2060" & model == "SSP585" &!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "2030_2060" & model == "SSP585" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(14,15), bold = T) | source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 4.8 | 9.2 | 15.0 | 7.5 | 36.5 | 37.6 | 25.6 | 0.3 |
| CESM | 5.8 | 11.5 | 15.6 | 5.2 | 38.1 | 36.0 | 25.7 | 0.3 |
| CMCC | 4.4 | 8.0 | 11.5 | 6.5 | 30.4 | 44.8 | 24.5 | 0.3 |
| CMCC-ESM2 | 6.5 | 9.3 | 8.0 | 2.5 | 26.3 | 25.4 | 48.0 | 0.3 |
| CNRM | 6.2 | 13.3 | 12.4 | 6.9 | 38.8 | 40.0 | 21.0 | 0.2 |
| EC-Earth3 | 7.8 | 9.4 | 11.8 | 4.6 | 33.6 | 38.8 | 27.4 | 0.2 |
| FGOALS | 6.8 | 13.5 | 18.1 | 7.7 | 46.1 | 30.8 | 22.8 | 0.2 |
| GFDL-ESM4 | 6.1 | 9.4 | 9.7 | 4.7 | 29.9 | 36.4 | 33.4 | 0.2 |
| INM | 4.6 | 8.4 | 14.0 | 6.0 | 33.0 | 40.6 | 26.2 | 0.3 |
| INM-CM5 | 3.5 | 8.6 | 12.4 | 5.8 | 30.3 | 43.6 | 25.8 | 0.3 |
| MPI | 8.6 | 12.4 | 11.1 | 4.5 | 36.6 | 37.0 | 26.2 | 0.2 |
| MRI | 6.0 | 11.1 | 9.6 | 4.0 | 30.7 | 40.8 | 28.1 | 0.3 |
| NorESM-2-MM | 5.5 | 11.4 | 17.6 | 5.9 | 40.4 | 33.2 | 26.2 | 0.3 |
| mean | 4.1 | 7.3 | 8.9 | 3.8 | 24.1 | 26.0 | 47.3 | 2.6 |
| sd | 1.0 | 1.3 | 2.1 | 1.0 | 5.4 | 3.7 | 4.7 | 0.0 |
3.6.2 2070-2100
map_list <- list()
for(i in unique(cmip6$source)){
g <- ggplot(data = subset(cmip6, period == "2070_2100" & model == "SSP585" & source == i)) + geom_raster(aes(x=lon, y = lat, fill = cat.AI))+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, "2070-2100", sep = ", "), fill = "")+
theme_void()
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 3, nrow = 5, common.legend = T, legend = "bottom")
sdmm <- cmip6 %>% subset(period == "2070_2100" & model == "SSP585" &!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
ungroup() %>% group_by(cat.AI) %>% summarise(mean = round(mean(percent, na.rm = T),1), sd = round(sd(percent, na.rm = T),1)) %>%
t() %>% as.data.frame() %>% setNames(c(.[1,1:6], "NA")) %>% .[-1, ] %>% sapply(as.numeric) %>% as.data.frame() %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")]), "source" = c("mean","sd")) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
tab <- cmip6 %>% subset(period == "2070_2100" & model == "SSP585" & !Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
group_by(source, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(source) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(source~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("source","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA")) %>%
rbind(sdmm)
knitr::kable(tab, digits = 2, escape = F) %>%
kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(6,9), italic = T, include_thead = T) %>% row_spec(c(13,14), bold = T) | source | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA |
|---|---|---|---|---|---|---|---|---|
| CAS-ESM2 | 6.1 | 10.8 | 15.9 | 6.5 | 39.3 | 37.5 | 22.9 | 0.3 |
| CMCC | 4.4 | 9.3 | 13.5 | 6.7 | 33.9 | 46.7 | 19.2 | 0.3 |
| CMCC-ESM2 | 5.6 | 10.5 | 8.9 | 2.9 | 27.9 | 28.1 | 43.8 | 0.3 |
| CNRM | 6.0 | 13.6 | 12.8 | 6.4 | 38.8 | 40.9 | 20.1 | 0.2 |
| EC-Earth3 | 7.7 | 9.6 | 13.0 | 4.9 | 35.2 | 40.5 | 23.9 | 0.2 |
| FGOALS | 15.6 | 15.0 | 18.3 | 6.8 | 55.7 | 24.4 | 19.8 | 0.2 |
| GFDL-ESM4 | 6.5 | 10.0 | 10.8 | 4.8 | 32.1 | 37.6 | 30.1 | 0.2 |
| INM | 4.4 | 8.7 | 14.8 | 6.0 | 33.9 | 41.7 | 24.1 | 0.3 |
| INM-CM5 | 3.9 | 9.8 | 12.0 | 6.0 | 31.7 | 44.1 | 23.9 | 0.3 |
| MPI | 8.7 | 12.3 | 11.1 | 4.7 | 36.8 | 36.8 | 26.2 | 0.2 |
| MRI | 7.1 | 11.5 | 10.6 | 4.1 | 33.3 | 40.1 | 26.4 | 0.3 |
| NorESM-2-MM | 6.4 | 12.8 | 17.4 | 6.5 | 43.1 | 34.3 | 22.3 | 0.3 |
| mean | 4.8 | 7.8 | 9.2 | 3.8 | 25.6 | 26.2 | 45.5 | 2.6 |
| sd | 2.1 | 1.3 | 2.0 | 0.8 | 6.2 | 4.4 | 4.6 | 0.0 |
4 Multimodel average
Here we compute the multimodel mean and median aridity index. We compare the category obtained by using the mean aridity index (cat.mean) to the category obtained in most models (cat.maj).
4.1 Summary dataframe
4.1.1 Difference with AI ref for all SSP
Compute the difference between future AI, precipitation and temperature for future scenarios, for all models.
cmip6 <- read.table("cmip6_AI.txt")
cmip6.ref <- cmip6 %>% filter(period == "1970_2000") %>% select(c("lon","lat","model","period","source","AI","spr","t","sET0")) %>% setNames(c("lon","lat","model","period","source","AI.ref","spr.ref","t.ref","ET0.ref"))
cmip6ext <- merge(cmip6, select(cmip6.ref, c("lon","lat","source","AI.ref","spr.ref","t.ref","ET0.ref")) , by = c("lon","lat","source"), all = T) %>% mutate(diff.AI = AI - AI.ref, diff.pr = spr - spr.ref, diff.t = t - t.ref, diff.ET0 = sET0 - ET0.ref)
write.table(cmip6ext, "cmip6ext.txt")Create a summary table with multimodel average.
cmip6 <- read.table("cmip6ext.txt")
cmip6s <- cmip6 %>%
group_by(lon,lat,Continent, Type, Name, Acronym, lm, model, period, z) %>%
dplyr::summarise(t.mean = mean(t, na.rm = T), t.sd = sd(t, na.rm = T), # t in °C
pr.mean = mean(spr, na.rm = T) , pr.sd = sd(spr, na.rm = T), # pr in mm/y
sfcWind.mean = mean(sfcWind, na.rm = T), sfcWind.sd = sd(sfcWind, na.rm = T),
Rn.mean = mean(Rn, na.rm = T), Rn.sd = sd(Rn, na.rm = T), # in MJ/m2/day
ea.mean = mean(ea, na.rm = T), ea.sd = sd(ea, na.rm = T), # in kPa
ET0 = mean(sET0, na.rm = T), ET0.sd = sd(sET0, na.rm = T), # in mm/y
ET0.ref = mean(ET0.ref, na.rm = T), ET0.ref.sd = sd(ET0.ref, na.rm = T), # in mm/y
diff.ET0.mean = mean(diff.ET0, na.rm = T), diff.ET0.sd = sd(diff.ET0, na.rm = T),
AI.mean = mean(AI, na.rm = T), AI.sd = sd(AI, na.rm = T),
AI.med = median(AI, na.rm = T),
AI.ref.mean = mean(AI.ref, na.rm = T), AI.ref.sd = sd(AI.ref, na.rm = T),
AI.ref.med = median(AI.ref, na.rm = T),
diff.AI.mean = mean(diff.AI, na.rm = T), diff.AI.sd = sd(diff.AI, na.rm = T),
diff.AI.med = median(diff.AI, na.rm = T),
spr.ref.mean = mean(spr.ref, na.rm = T), spr.ref.sd = sd(spr.ref, na.rm = T),
diff.pr.mean = mean(diff.pr, na.rm = T), diff.pr.sd = sd(diff.pr, na.rm = T),
t.ref.mean = mean(t.ref, na.rm = T), t.ref.sd = sd(t.ref, na.rm =T),
diff.t.mean = mean(diff.t, na.rm = T), diff.t.sd = sd(diff.t, na.rm = T),
Hyperarid = sum(cat.AI == "Hyperarid"),
Arid = sum(cat.AI == "Arid"),
'Semi-arid' = sum(cat.AI == "Semi-arid"),
'Dry subhumid' = sum(cat.AI == "Dry subhumid") ,
'Humid' = sum(cat.AI == "Humid"),
'Cold' = sum(cat.AI == "Cold"),
'NA' = sum(is.na(cat.AI)),
nb.models = n(),
plus.AI = table(diff.AI > 0)["TRUE"],
minus.AI = table(diff.AI < 0)["TRUE"]) %>%
ungroup() %>% as.data.frame()
cmip6s$cat.maj <- colnames(select(cmip6s, c("Hyperarid","Arid",'Semi-arid',"Dry subhumid",'Humid',"Cold","NA")))[max.col(select(cmip6s, c("Hyperarid","Arid",'Semi-arid',"Dry subhumid",'Humid',"Cold","NA")),ties.method = "first")]
cmip6s$nb.maj <- cmip6s %>% select(c("Hyperarid","Arid",'Semi-arid',"Dry subhumid",'Humid',"Cold","NA")) %>% apply(1, max)
cmip6s$prop.maj <- with(cmip6s, nb.maj / nb.models)
breaks.unesco <- c(-Inf,0,0.03,0.2,0.5,0.65,Inf)
cat.unesco <- c("(0,0.03]"= "Hyperarid", "(0.03,0.2]" = "Arid", "(0.2,0.5]" = "Semi-arid", "(0.5,0.65]" = "Dry subhumid","(0.65, Inf]" = "Humid", "(-Inf,0]" = "Cold")
cmip6s$cat.AI <- cmip6s$AI.mean %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6s$cat.AI[which(cmip6s$ET0 < 400)] <- "Cold"
cmip6s$cat.AI.ref <- cmip6s$AI.ref.mean %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6s$cat.AI.ref[which(cmip6s$ET0.ref < 400)] <- "Cold"
cmip6s$same.catAI <- with(cmip6s, ifelse(cat.maj == cat.AI, "same", "diff"))
cmip6s$cat.dryer <- with(cmip6s,
ifelse(cat.AI.ref == "Arid" & cat.AI %in% c("Cold","Humid","Dry subhumid","Semi-arid"), "wetter",
ifelse(cat.AI.ref == "Arid" & cat.AI == "Hyperarid", "dryer",
ifelse(cat.AI.ref == "Semi-arid" & cat.AI %in% c("Cold","Humid","Dry subhumid"), "wetter",
ifelse(cat.AI.ref == "Semi-arid" & cat.AI %in% c("Hyperarid","Arid"), "dryer",
ifelse(cat.AI.ref == "Dry subhumid" & cat.AI %in% c("Cold","Humid"), "wetter",
ifelse(cat.AI.ref == "Dry subhumid" & cat.AI %in% c("Semi-arid","Arid","Hyperarid"), "dryer",
ifelse(cat.AI.ref == "Humid" & cat.AI == "Cold","wetter",
ifelse(cat.AI.ref == "Humid" & cat.AI %in% c("Dry subhumid","Semi-arid","Arid","Hyperarid"), "dryer",
ifelse(cat.AI.ref == "Cold" & cat.AI %in% c("Humid","Dry subhumid","Semi-arid","Arid","Hyperarid"), "dryer",
ifelse(cat.AI.ref == "Hyperarid" & cat.AI %in% c("Cold","Humid","Dry subhumid","Semi-arid","Arid"), "wetter",
ifelse(cat.AI.ref == cat.AI, "same",
NA))))))))))))
cmip6sref <- cmip6s %>% filter(period == "1970_2000") %>% select(c("lon","lat","model","period","cat.maj")) %>% setNames(c("lon","lat","model","period","cat.maj.ref"))
cmip6s <- merge(cmip6s, select(cmip6sref, c("lon","lat","cat.maj.ref")),
by = c("lon","lat"), all.x = T)
cmip6s$cat.maj.dryer <- with(cmip6s,
ifelse(cat.maj.ref == "Arid" & cat.maj %in% c("Cold","Humid","Dry subhumid","Semi-arid"), "wetter",
ifelse(cat.maj.ref == "Arid" & cat.maj == "Hyperarid", "dryer",
ifelse(cat.maj.ref == "Semi-arid" & cat.maj %in% c("Cold","Humid","Dry subhumid"), "wetter",
ifelse(cat.maj.ref == "Semi-arid" & cat.maj %in% c("Hyperarid","Arid"), "dryer",
ifelse(cat.maj.ref == "Dry subhumid" & cat.maj %in% c("Cold","Humid"), "wetter",
ifelse(cat.maj.ref == "Dry subhumid" & cat.maj %in% c("Semi-arid","Arid","Hyperarid"), "dryer",
ifelse(cat.maj.ref == "Humid" & cat.maj == "Cold","wetter",
ifelse(cat.maj.ref == "Humid" & cat.maj %in% c("Dry subhumid","Semi-arid","Arid","Hyperarid"), "dryer",
ifelse(cat.maj.ref == "Cold" & cat.maj %in% c("Humid","Dry subhumid","Semi-arid","Arid","Hyperarid"), "dryer",
ifelse(cat.maj.ref == "Hyperarid" & cat.maj %in% c("Cold","Humid","Dry subhumid","Semi-arid","Arid"), "wetter",
ifelse(cat.maj.ref == cat.maj, "same",
NA))))))))))))
cmip6s$Continent[which(cmip6s$Continent == "EUROPE-AFRICA")] <- "MED-BASIN"
write.table(cmip6s, "cmip6s.txt")
cmip6s <- read.table("cmip6s.txt")4.2 Compare cat mean and cat maj
cat mean = category of aridity obtained by using the multimodel mean AI cat maj = category of aridity obtained by using the category majoritarily obtained in the 13 models
4.2.1 Where are cat mean and cat maj different
cmip6s %>% group_by(model, period) %>% summarise(maj.equal.mean = table(same.catAI)[2], maj.diff.mean = table(same.catAI)[1], prop.same = round(maj.equal.mean/n()*100,1)) %>% knitr::kable(col.names = c("Model","Period","Number of gridcells in which cat.maj = cat.mean", "Number of gridcells in which cat.maj /= cat.mean", "Proportion of cells with the same category (%)")) %>% kableExtra::kable_styling(bootstrap_options = "bordered")| Model | Period | Number of gridcells in which cat.maj = cat.mean | Number of gridcells in which cat.maj /= cat.mean | Proportion of cells with the same category (%) |
|---|---|---|---|---|
| SSP245 | 2030_2060 | 19884 | 1798 | 89.3 |
| SSP245 | 2070_2100 | 19890 | 1792 | 89.3 |
| SSP370 | 2030_2060 | 19988 | 1694 | 89.8 |
| SSP370 | 2070_2100 | 19911 | 1771 | 89.4 |
| SSP585 | 2030_2060 | 19799 | 1883 | 88.9 |
| SSP585 | 2070_2100 | 19796 | 1886 | 88.9 |
| historical | 1850_1880 | 19907 | 1775 | 89.4 |
| historical | 1970_2000 | 19880 | 1802 | 89.3 |
| historical | 1985_2015 | 19936 | 1746 | 89.6 |
cmip6s$cat.AI <- factor(cmip6s$cat.AI, levels = c("Hyperarid","Arid","Semi-arid","Dry subhumid", "Humid","Cold"))
cmip6s$cat.maj <- factor(cmip6s$cat.maj, levels = c("Hyperarid","Arid","Semi-arid","Dry subhumid", "Humid","Cold"))
ggplot(subset(cmip6s, cat.AI != "Cold"))+
geom_col(aes(x= period, y = cat.AI, fill = cat.AI), just = -0.2, width = 0.3)+
geom_col(aes(x = period, y = cat.maj, fill = cat.maj), just = 1.2, width = 0.3)+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(fill = "", y ="")+
facet_grid(rows = vars(model), switch = "y")+
theme_minimal()+theme(axis.text.y = element_blank())
tab <- rbind(table(cmip6s$cat.AI)/length(cmip6s$cat.AI)*100, table(cmip6s$cat.maj)/length(cmip6s$cat.maj)*100) %>% t() %>% as.data.frame() %>% setNames(c("Cat mean", "Cat maj"))
knitr::kable(tab, digits =2, caption = "Proportion of gridcells in each aridity category") %>% kable_styling(bootstrap_options = "bordered")| Cat mean | Cat maj | |
|---|---|---|
| Hyperarid | 3.51 | 4.18 |
| Arid | 6.25 | 7.43 |
| Semi-arid | 7.63 | 9.19 |
| Dry subhumid | 3.39 | 0.84 |
| Humid | 28.75 | 28.53 |
| Cold | 47.90 | 47.23 |
map_list <- list()
for(i in unique(cmip6s$model)){
df <- subset(cmip6s, model == i)
for(j in unique(df$period)){
index <- paste(i, j, sep = "")
g <- ggplot()+geom_raster(data = subset(df, period == j & same.catAI == "diff"), aes(x = lon, y = lat, fill = cat.AI))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, j , sep = " "), fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}
}
col1 <- ggarrange(plotlist = map_list, nrow = 9, ncol = 1, common.legend = T, legend = "bottom") %>% annotate_figure(top = "cat.mean for gridcells in which\ncat.maj and cat.mean are different")
map_list2 <- list()
for(i in unique(cmip6s$model)){
df <- subset(cmip6s, model == i)
for(j in unique(df$period)){
index <- paste(i, j, sep = "")
g <- ggplot()+geom_raster(data = subset(df, period == j & same.catAI == "diff"), aes(x = lon, y = lat, fill = cat.maj))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste(i, j , sep = " "), fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list2[[index]] <- g
}
}
col2 <- ggarrange(plotlist = map_list2, nrow = 9, ncol = 1, common.legend = T, legend = "bottom") %>% annotate_figure(top = "cat.maj for gridcells in which\ncat.maj and cat.mean are different")
ggarrange(col1,col2,ncol = 2, common.legend = T)
4.2.2 Plot cat maj and cat mean for reference period
ggpubr::ggarrange(plotlist = list(
ggplot() +
geom_raster(data = subset(cmip6s, period == "1970_2000"), aes(x=lon, y = lat, fill = cat.maj))+
geom_point(data = subset(cmip6s, period == "1970_2000" & nb.maj > 10), aes(x =lon, y = lat), shape = "'", size = 0.5, col = "grey60")+
scale_fill_manual(values = col.cat)+
labs(title = "Category chosen in majority by models", fill = "")+
ylim(-55,90)+
theme_void(base_size = 15)+
theme(legend.position = "none"),
ggplot() +
geom_raster(data = subset(cmip6s, period == "1970_2000"), aes(x=lon, y = lat, fill = cat.AI))+
geom_point(data = subset(cmip6s, period == "1970_2000" & nb.maj > 10), aes(x =lon, y = lat), shape = "'", size = 0.5, col = "grey60")+
scale_fill_manual(values = col.cat)+
labs(title = "Multimodel average categories of AI", fill = "")+
ylim(-55,90)+
theme_void(base_size = 15)+
theme(legend.position = "none")),
ncol = 2)
map_list <- list()
for(i in c("1850_1880","1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = cat.maj))+
geom_point(data = subset(cmip6s, period == i & model == "historical" & nb.maj > 10), aes(x= lon, y = lat), shape = "'", size = 0.5, col = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = i, fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[i]] <- g
}
map_list2 <- list()
for(i in c("1850_1880","1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = cat.AI))+
# geom_point(data = subset(cmip6s, period == i & model == "historical" & nb.maj > 10), aes(x= lon, y = lat), size = 0.5, col = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = i, fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list2[[i]] <- g
}
ggarrange(
annotate_figure(ggarrange(plotlist = map_list, nrow = 3, common.legend = T, legend = "bottom"), top = "Majority category among models"),
annotate_figure(ggarrange(plotlist = map_list2, nrow = 3, common.legend = T, legend = "bottom"), top = "Multimodel average category"),
ncol = 2, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = cat.maj))+
geom_point(data = subset(cmip6s, period == j & model == i & nb.maj > 10), aes(x= lon, y = lat), shape = "'", size = 0.5, col = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(fill = "Cat AI maj", title = paste(i, j, sep = ", "))+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
map_list2 <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = cat.AI))+
# geom_point(data = subset(cmip6s, period == j & model == i & nb.maj > 10), aes(x= lon, y = lat), shape = "'", size = 0.5, col = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(fill = "Cat AI mean", title = paste(i, j, sep = ", "))+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list2[[index]] <- g
}}
ggarrange(plotlist = map_list, nrow = 3, ncol = 2, common.legend = T, legend = "bottom")
ggarrange(plotlist = map_list2, nrow = 3, ncol = 2, common.legend = T, legend = "bottom")
4.3 Multimodel maps of cat mean
cmip6s$cat.AI <- factor(cmip6s$cat.AI, levels = c("Hyperarid","Arid","Semi-arid","Dry subhumid","Humid","Cold"))
map_list <- list()
for(i in c("1850_1880", "1970_2000","1985_2015")){
g <- ggplot() +
geom_tile(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = cat.AI))+
geom_point(data = subset(cmip6s,nb.maj > 10), aes(x = lon, y = lat), shape = ".", col = "grey20", size = 0.1)+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste("Category AI",i , sep = " "), fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 1, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() +
geom_tile(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = cat.AI))+
geom_point(data = subset(cmip6s,nb.maj > 10), aes(x = lon, y = lat), shape = ".", col = "grey20", size = 0.1)+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = paste("Category AI",index, sep = " "), fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
cmip6s$cat.AI <- factor(cmip6s$cat.AI, levels = c("Hyperarid","Arid","Semi-arid","Dry subhumid","Humid","Cold"))
ggplot() +
geom_tile(data = subset(cmip6s, period == "1970_2000" & model == "historical"), aes(x=lon, y = lat, fill = cat.AI))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = "Category AI 1970-2000", fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
ggplot() +
geom_tile(data = subset(cmip6s, period == "2070_2100" & model == "SSP585"), aes(x=lon, y = lat, fill = cat.AI))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = "Category AI 2070-2100, SSP 585", fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[i]] <- g
4.4 Table of multimodel average category in percent
# cmip6s$cat.AI <- cmip6s$AI.mean %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
# cmip6s$cat.AI[which(cmip6s$ET0 < 400)] <- "Cold"
cmip6s$cat.AI.med <- cmip6s$AI.med %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6s$cat.AI.med[which(cmip6s$ET0 < 400)] <- "Cold"
table.percent <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
select(period, model, cat.AI) %>% group_by(period, model, cat.AI) %>% summarise(count = n()) %>%
ungroup() %>% group_by(period, model) %>% mutate(percent = round(count/sum(count)*100,1))
table.percent.med <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55) %>%
select(period, model, cat.AI.med) %>% setNames(c("period","model","cat.AI")) %>% group_by(period, model, cat.AI) %>% summarise(count.med = n()) %>%
ungroup() %>% group_by(period, model) %>% mutate(percent.med = round(count.med/sum(count.med)*100,1))
table <- merge(table.percent, table.percent.med, by = c("period","model","cat.AI"), all.x = T)
kable(table)| period | model | cat.AI | count | percent | count.med | percent.med |
|---|---|---|---|---|---|---|
| 1850_1880 | historical | Hyperarid | 746 | 4.8 | 906 | 5.9 |
| 1850_1880 | historical | Arid | 1340 | 8.7 | 1450 | 9.4 |
| 1850_1880 | historical | Semi-arid | 1577 | 10.2 | 1742 | 11.3 |
| 1850_1880 | historical | Dry subhumid | 729 | 4.7 | 767 | 5.0 |
| 1850_1880 | historical | Humid | 6385 | 41.3 | 5912 | 38.3 |
| 1850_1880 | historical | Cold | 4631 | 30.0 | NA | NA |
| 1850_1880 | historical | NA | 36 | 0.2 | 4667 | 30.2 |
| 1970_2000 | historical | Hyperarid | 739 | 4.8 | 897 | 5.8 |
| 1970_2000 | historical | Arid | 1340 | 8.7 | 1450 | 9.4 |
| 1970_2000 | historical | Semi-arid | 1559 | 10.1 | 1681 | 10.9 |
| 1970_2000 | historical | Dry subhumid | 729 | 4.7 | 767 | 5.0 |
| 1970_2000 | historical | Humid | 6174 | 40.0 | 5746 | 37.2 |
| 1970_2000 | historical | Cold | 4867 | 31.5 | NA | NA |
| 1970_2000 | historical | NA | 36 | 0.2 | 4903 | 31.7 |
| 1985_2015 | historical | Hyperarid | 732 | 4.7 | 879 | 5.7 |
| 1985_2015 | historical | Arid | 1345 | 8.7 | 1431 | 9.3 |
| 1985_2015 | historical | Semi-arid | 1541 | 10.0 | 1703 | 11.0 |
| 1985_2015 | historical | Dry subhumid | 723 | 4.7 | 733 | 4.7 |
| 1985_2015 | historical | Humid | 6062 | 39.3 | 5657 | 36.6 |
| 1985_2015 | historical | Cold | 5005 | 32.4 | NA | NA |
| 1985_2015 | historical | NA | 36 | 0.2 | 5041 | 32.6 |
| 2030_2060 | SSP245 | Hyperarid | 772 | 5.0 | 882 | 5.7 |
| 2030_2060 | SSP245 | Arid | 1401 | 9.1 | 1606 | 10.4 |
| 2030_2060 | SSP245 | Semi-arid | 1731 | 11.2 | 1833 | 11.9 |
| 2030_2060 | SSP245 | Dry subhumid | 729 | 4.7 | 833 | 5.4 |
| 2030_2060 | SSP245 | Humid | 6397 | 41.4 | 5876 | 38.0 |
| 2030_2060 | SSP245 | Cold | 4378 | 28.3 | NA | NA |
| 2030_2060 | SSP245 | NA | 36 | 0.2 | 4414 | 28.6 |
| 2030_2060 | SSP370 | Hyperarid | 717 | 4.6 | 886 | 5.7 |
| 2030_2060 | SSP370 | Arid | 1483 | 9.6 | 1532 | 9.9 |
| 2030_2060 | SSP370 | Semi-arid | 1686 | 10.9 | 1810 | 11.7 |
| 2030_2060 | SSP370 | Dry subhumid | 676 | 4.4 | 843 | 5.5 |
| 2030_2060 | SSP370 | Humid | 6394 | 41.4 | 5885 | 38.1 |
| 2030_2060 | SSP370 | Cold | 4452 | 28.8 | NA | NA |
| 2030_2060 | SSP370 | NA | 36 | 0.2 | 4488 | 29.1 |
| 2030_2060 | SSP585 | Hyperarid | 792 | 5.1 | 909 | 5.9 |
| 2030_2060 | SSP585 | Arid | 1369 | 8.9 | 1569 | 10.2 |
| 2030_2060 | SSP585 | Semi-arid | 1759 | 11.4 | 1903 | 12.3 |
| 2030_2060 | SSP585 | Dry subhumid | 782 | 5.1 | 899 | 5.8 |
| 2030_2060 | SSP585 | Humid | 6502 | 42.1 | 5924 | 38.4 |
| 2030_2060 | SSP585 | Cold | 4204 | 27.2 | NA | NA |
| 2030_2060 | SSP585 | NA | 36 | 0.2 | 4240 | 27.5 |
| 2070_2100 | SSP245 | Hyperarid | 792 | 5.1 | 920 | 6.0 |
| 2070_2100 | SSP245 | Arid | 1431 | 9.3 | 1586 | 10.3 |
| 2070_2100 | SSP245 | Semi-arid | 1742 | 11.3 | 1876 | 12.1 |
| 2070_2100 | SSP245 | Dry subhumid | 777 | 5.0 | 878 | 5.7 |
| 2070_2100 | SSP245 | Humid | 6532 | 42.3 | 6014 | 38.9 |
| 2070_2100 | SSP245 | Cold | 4134 | 26.8 | NA | NA |
| 2070_2100 | SSP245 | NA | 36 | 0.2 | 4170 | 27.0 |
| 2070_2100 | SSP370 | Hyperarid | 899 | 5.8 | 992 | 6.4 |
| 2070_2100 | SSP370 | Arid | 1399 | 9.1 | 1594 | 10.3 |
| 2070_2100 | SSP370 | Semi-arid | 1842 | 11.9 | 1935 | 12.5 |
| 2070_2100 | SSP370 | Dry subhumid | 822 | 5.3 | 918 | 5.9 |
| 2070_2100 | SSP370 | Humid | 6324 | 40.9 | 5847 | 37.9 |
| 2070_2100 | SSP370 | Cold | 4122 | 26.7 | NA | NA |
| 2070_2100 | SSP370 | NA | 36 | 0.2 | 4158 | 26.9 |
| 2070_2100 | SSP585 | Hyperarid | 852 | 5.5 | 964 | 6.2 |
| 2070_2100 | SSP585 | Arid | 1412 | 9.1 | 1614 | 10.5 |
| 2070_2100 | SSP585 | Semi-arid | 1857 | 12.0 | 1941 | 12.6 |
| 2070_2100 | SSP585 | Dry subhumid | 805 | 5.2 | 905 | 5.9 |
| 2070_2100 | SSP585 | Humid | 6686 | 43.3 | 6188 | 40.1 |
| 2070_2100 | SSP585 | Cold | 3796 | 24.6 | NA | NA |
| 2070_2100 | SSP585 | NA | 36 | 0.2 | 3832 | 24.8 |
ggplot(subset(table, period == "1970_2000"), aes(x = cat.AI))+
geom_col(aes(y = percent, fill = "mean"), position = position_nudge(x = - 0.2), width = 0.4)+
geom_col(aes(y = percent.med, fill = "med"), position = position_nudge(x = + 0.2), width = 0.4)+
scale_fill_manual(values = c("#ABD9E9", "#FDAE61"))+
labs(x="Aridity category", fill = "Using mean or median")+
theme_minimal()
5 Maps multimodel AI
quantile(cmip6s$AI.mean, probs = seq(0,1,0.1), na.rm = T)
## 0% 10% 20% 30% 40% 50%
## 2.902197e-03 1.993698e-01 5.922059e-01 1.019621e+00 1.457766e+00 2.127218e+00
## 60% 70% 80% 90% 100%
## 3.578980e+00 1.168470e+01 2.438867e+01 4.856852e+01 5.329875e+06
ai.breaks <- c(0,0.03,0.2,0.5,0.65,1,10,20,30)
colscale <- brewer.pal(n = 11, name ="RdYlBu")[-c(5,6)]map_list <- list()
for(i in c("1850_1880", "1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = AI.mean))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = ai.breaks, palette = function(x) colscale,
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(title = i, fill = "Aridity index")+
theme_void()+ylim(-55,90)+
theme(legend.position = "right")
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 1, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = AI.mean))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = ai.breaks, palette = function(x) colscale,
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(fill = "Aridity index", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
6 AI changes over time and scenarios
v<-NULL
v2<-NULL
for (N in 5:30) {
v<-c(v,sum(sapply(floor(2*N/3):N,function(i) choose(N,i)*(1/2)^N*2)))# proba d'avoir 2/3 de même signe à partir de N modèles, lorsque N change
v2<-c(v2,sum(sapply(floor(2*N/3):N,function(i) choose(N,i)*(1/2)^N)))# proba d'avoir 2/3 de signe >0 à partir de N modèles, lorsque N change
}
plot(5:30,v,type="l",ylab="Proba(>2/3 models...)",xlab="Nb of models")
lines(5:30,v2,col="red")
6.1 Changes AI for summary CMIP6
6.2 Boxplot of changes AI
ggpubr::ggarrange(plotlist = list(
ggplot(subset(cmip6s, diff.AI.mean < 1 & diff.AI.mean > -1 & model == "historical"))+
geom_boxplot(aes(x=period, y = - diff.AI.mean, col = period), outliers = F)+
scale_color_manual(values = c("#D73027","#FDAE61","#FEE08B"))+
labs(x = "", y = "Difference of AI between each period and\nthe reference period 1970-2000")+
facet_grid(cols = vars(model))+
theme_minimal() + theme(legend.position = "none", axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)),
ggplot(subset(cmip6s, diff.AI.mean < 1 & diff.AI.mean > -1 & model != "historical"))+
geom_boxplot(aes(x=period, y = diff.AI.mean, col = period), outliers = F)+
scale_color_manual(values = c("#ABD9E9","#4575B4"))+
scale_y_continuous(position = "right")+
labs(y = "", x = "")+
facet_grid(cols = vars(model))+
theme_minimal() + theme(legend.position = "none", axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))),
widths = c(1.5,3),
ncol = 2, nrow = 1
)
6.3 Strictly wetter or dryer (reference period: 1970-2000)
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = ", ")
g <- ggplot()+
geom_point(data = subset(cmip6s, model == i & period == j & plus.AI > 10), aes(x = lon, y = lat, col = "plus"), shape = "+")+
geom_point(data = subset(cmip6s, model == i & period == j & minus.AI > 10), aes(x = lon, y = lat, col = "minus"), shape = "-")+
scale_color_manual(values = c("#ABD9E9", "#D73027"))+
labs(color = "", x = "", y = "", title = index)+
theme_minimal()
map_list[[index]] <- g
}
}
ggarrange(plotlist = map_list, ncol = 2, nrow = 3, common.legend = T)
map_list <- list()
for(i in c("1850_1880","1985_2015")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_tile(data = subset(cmip6s, period == i & model == "historical" & diff.AI.mean > 0), aes(x=lon, y = lat, fill = "wetter"))+
geom_tile(data = subset(cmip6s, period == i & model == "historical" & diff.AI.mean < 0), aes(x=lon, y = lat, fill = "dryer"))+
geom_point(data = subset(cmip6s, period == i & model == "historical" & plus.AI > 10), aes(x=lon, y = lat), shape = "+", size = 0.5)+
geom_point(data = subset(cmip6s, period == i & model == "historical" & minus.AI > 10), aes(x=lon, y = lat), shape = "-", size = 0.5)+
borders(colour = "grey60")+
scale_fill_manual(values = c("#FDAE61", "#ABD9E9"), na.translate = F)+
labs(title = paste("AI changes between",i , "and 1970-2000", sep = " "), fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 2)
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i & diff.AI.mean > 0), aes(x=lon, y = lat, fill = "wetter"))+
geom_tile(data = subset(cmip6s, period == j & model == i & diff.AI.mean < 0), aes(x=lon, y = lat, fill = "dryer"))+
geom_point(data = subset(cmip6s, period == j & model == i & plus.AI > 10), aes(x=lon, y = lat), shape = "+", col = "grey20", size = 0.5)+
geom_point(data = subset(cmip6s, period == j & model == i & minus.AI > 10), aes(x=lon, y = lat), shape = "-", col = "grey20", size = 0.5)+
borders(colour = "grey60")+
scale_fill_manual(values = c("#F46D43", "#74ADD1"), na.translate = F)+
labs(fill = "Compared to 1970-2000", title = i)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
6.4 Wetter or dryer, binned (reference period: 1970-2000)
ano.quantiles <- cmip6s %>% group_by(model, period) %>% summarise(q10 = quantile(diff.AI.mean, probs = 0.1, na.rm =T),
q25 = quantile(diff.AI.mean, probs = 0.25, na.rm =T),
q50 = quantile(diff.AI.mean, probs = 0.5, na.rm =T),
q75 = quantile(diff.AI.mean, probs = 0.75, na.rm =T),
q90 = quantile(diff.AI.mean, probs = 0.9, na.rm =T))
ano.quantiles <- cmip6s %>% summarise(q10 = quantile(diff.AI.mean, probs = 0.1, na.rm =T),
q25 = quantile(diff.AI.mean, probs = 0.25, na.rm =T),
q50 = quantile(diff.AI.mean, probs = 0.5, na.rm =T),
q75 = quantile(diff.AI.mean, probs = 0.75, na.rm =T),
q90 = quantile(diff.AI.mean, probs = 0.9, na.rm =T))
qbreaks <- c(-5, -1,-0.1, -0.01, 0, 0.01, 0.1, 1,5)
colscale <- c("#08519c", "#3182bd", "#6baed6", "#9ecae1", "#c6dbef", "#fddbc7", "#f4a582", "#d6604d", "#b2182b", "#67001f")“diff.AI.mean” is the multimodel AI average minus the reference AI (period 1970-2000). More blue: AI mean is superior to AI ref, hence wetter than the ref. More red: the anomalie is negative, hence AI mean is inferior to AI ref, hence dryer.
AS OF NOW THE SAME QUANTILES ARE USED FOR ALL MAPS.
map_list <- list()
for(i in c("1850_1880","1985_2015")){
#quant = subset(ano.quantiles, model == "historical" & period == i)
#b = quant[1,c(3:7)] %>% as.numeric
b = qbreaks
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = diff.AI.mean))+
borders(colour = "grey60")+
geom_point(data = subset(cmip6s, period == i & model == "historical" & plus.AI > 10), aes(x=lon, y = lat), shape = "+", col = "grey20", size = 0.5)+
geom_point(data = subset(cmip6s, period == i & model == "historical" & minus.AI > 10), aes(x=lon, y = lat), shape = "-", col = "grey20", size = 0.5)+
binned_scale(aesthetics = "fill", breaks = b, palette = function(x) rev(colscale),
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(title = i, fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "right")
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 2, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
b = qbreaks
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = diff.AI.mean))+
borders(colour = "grey60")+
geom_point(data = subset(cmip6s, period == i & model == "historical" & plus.AI > 10), aes(x=lon, y = lat), shape = "+", col = "grey20", size = 0.5)+
geom_point(data = subset(cmip6s, period == i & model == "historical" & minus.AI > 10), aes(x=lon, y = lat), shape = "-", col = "grey20", size = 0.5)+
binned_scale(aesthetics = "fill", breaks = b, palette = function(x) rev(colscale),
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(fill = "Compared to 1970-2000", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
6.5 Wetter or dryer, binned, % (reference period: 1970-2000)
cmip6s$diff.AI.mean.percent <- with(cmip6s, (AI.mean-AI.ref.mean)/AI.ref.mean*100)
pbreaks <- c(-40, -30,-20, -10, 0, 10, 20, 30, 40)
colscale <- c("#08519c", "#3182bd", "#6baed6", "#9ecae1", "#c6dbef","white","white", "#fddbc7", "#f4a582", "#d6604d", "#b2182b")“diff.AI.mean” is the multimodel AI average minus the reference AI (period 1970-2000). More blue: AI mean is superior to AI ref, hence wetter than the ref. More red: the anomalie is negative, hence AI mean is inferior to AI ref, hence dryer.
map_list <- list()
for(i in c("1850_1880","1985_2015")){
#quant = subset(ano.quantiles, model == "historical" & period == i)
#b = quant[1,c(3:7)] %>% as.numeric
b = pbreaks
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = diff.AI.mean.percent))+
borders(colour = "grey60")+
geom_point(data = subset(cmip6s, period == i & model == "historical" & plus.AI > 10), aes(x=lon, y = lat), shape = "+", col = "grey20", size = 0.5)+
geom_point(data = subset(cmip6s, period == i & model == "historical" & minus.AI > 10), aes(x=lon, y = lat), shape = "-", col = "grey20", size = 0.5)+
binned_scale(aesthetics = "fill", breaks = b, palette = function(x) rev(colscale))+
labs(title = i, fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "right")
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 2, common.legend = T, legend = "bottom") %>% annotate_figure(top = "% change of AI index compared to 1970-2000")
#,guide = guide_legend(label.theme = element_text(angle = 0))
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
b = pbreaks
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = diff.AI.mean.percent))+
borders(colour = "grey60", size = 0.5)+
geom_point(data = subset(cmip6s, period == j & model == i & plus.AI > 10), aes(x=lon, y = lat), shape = "+", col = "grey20", size = 0.5)+
geom_point(data = subset(cmip6s, period == j & model == i & minus.AI > 10), aes(x=lon, y = lat), shape = "-", col = "grey20", size = 0.5)+
binned_scale(aesthetics = "fill", breaks = b, palette = function(x) rev(colscale))+
labs(fill = "% change of AI index\ncompared to 1970-2000", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
ggsave("map_AIpercent_SSP585_2100.png", map_list[[index]])7 Maps of difference between future and reference category of AI, using cat maj
7.1 Detailed changes and colors cat maj
cmip6s$cat.maj[which(is.na(cmip6s$cat.maj) == T)] <- "NA"
cmip6s$cat.maj.ref[which(is.na(cmip6s$cat.maj.ref) == T)] <- "NA"
cmip6s$change.catmaj <- with(cmip6s, ifelse(cat.maj == cat.maj.ref, 0, 1))
cmip6s$change.cat.maj <- with(cmip6s, paste(cat.maj.ref, cat.maj, sep = " to "))
cmip6s$change.cat.maj <- factor(cmip6s$change.cat.maj,
levels = c("Cold to Humid",
"Cold to Dry subhumid",
"Cold to Semi-arid",
"Humid to Arid",
"Humid to Semi-arid",
"Humid to Dry subhumid",
"Dry subhumid to Semi-arid",
"Dry subhumid to Arid",
"Semi-arid to Arid",
"Arid to Hyperarid",
"Hyperarid to Arid",
"Arid to Semi-arid",
"Arid to Dry subhumid",
"Arid to Humid",
"Semi-arid to Dry subhumid",
"Semi-arid to Humid",
"Semi-arid to Cold",
"Dry subhumid to Humid",
"Dry subhumid to Cold",
"Humid to Cold"))
colors_changes <- c("Cold to Humid" = "#FEE08B",
"Cold to Dry subhumid" = "#FDCB6A",
"Cold to Semi-arid" = "#FDB463",
"Humid to Arid" = "#FDAE61",
"Humid to Semi-arid" = "#F68D54",
"Humid to Dry subhumid" = "#F46B43",
"Dry subhumid to Semi-arid" = "#F03B20",
"Dry subhumid to Arid" = "#E63125",
"Semi-arid to Arid" = "#D73027",
"Arid to Hyperarid" = "#BD0026",
"Hyperarid to Arid" = "#ABD9E9",
"Arid to Semi-arid" = "#91C4DC",
"Arid to Dry subhumid" = "#7FB2D0",
"Arid to Humid" = "#6A9EC4",
"Semi-arid to Dry subhumid" = "#5893B8",
"Semi-arid to Humid" = "#4575B4",
"Semi-arid to Cold" = "#5D83C9",
"Dry subhumid to Humid" = "#77A1E2",
"Dry subhumid to Cold" = "#A7C7E9",
"Humid to Cold" = "#DAE2F7")7.2 Changes towards dryer categories cat maj
7.2.1 Historical
map_list <- list()
for(i in c("1850_1880","1985_2015")){
g <- ggplot() +
geom_raster(data = subset(cmip6s, period == i & model == "historical" & change.catmaj == 1 & cat.maj.dryer == "dryer"),
aes(x=lon, y = lat, fill = change.cat.maj))+
borders(colour = "grey60")+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(title = i, fill = "Change of category\ncompared to\n1970-2000")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 3))
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 2, common.legend = T, legend = "bottom")
7.2.1.1 Future
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i & change.catmaj == 1 & cat.maj.dryer == "dryer"),
aes(x=lon, y = lat, fill = change.cat.maj))+
borders(colour = "grey60")+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(fill = "Change of category\ncompared to\n1970-2000", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 3))
map_list[[index]] <- g
}}
ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
7.3 Changes towards wetter categories cat maj
7.3.0.1 Historical
map_list <- list()
for(i in c("1850_1880","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical" & change.catmaj == 1 & cat.maj.dryer == "wetter"),
aes(x=lon, y = lat, fill = change.cat.maj))+
borders(colour = "grey60", ylim = c(-40,40))+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(title = i, fill = "Change of category\ncompared to\n1970-2000")+
theme_void()+
ylim(-50,50)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 3))
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 2, common.legend = T, legend = "bottom")
7.3.0.2 Future
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i & change.catmaj == 1 & cat.maj.dryer == "wetter"),
aes(x=lon, y = lat, fill = change.cat.maj))+
borders(colour = "grey60", ylim = c(-40,40))+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(fill = "Change of category\ncompared to\n1970-2000", title = index)+
theme_void()+ylim(-50,50)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 3))
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list, nrow = 3, ncol = 2, common.legend = T, legend = "bottom")
8 Map of difference between future and reference category of AI, using cat mean
8.1 Detailed changes and colors
cmip6s$change.catmean <- with(cmip6s, ifelse(cat.AI == cat.AI.ref, 0, 1))
cmip6s$change.cat.mean <- with(cmip6s, paste(cat.AI.ref, cat.AI, sep = " to "))
unique(cmip6s$change.cat.mean)
## [1] "NA to NA" "Cold to Cold"
## [3] "Semi-arid to Semi-arid" "Semi-arid to Arid"
## [5] "Arid to Arid" "Arid to Hyperarid"
## [7] "Hyperarid to Hyperarid" "Hyperarid to Arid"
## [9] "Dry subhumid to Dry subhumid" "Dry subhumid to Semi-arid"
## [11] "Humid to Semi-arid" "Dry subhumid to Humid"
## [13] "Humid to Humid" "Humid to Dry subhumid"
## [15] "Semi-arid to Dry subhumid" "Arid to Semi-arid"
## [17] "Cold to Humid" "Humid to Cold"
table(cmip6s$change.cat.mean)
##
## Arid to Arid Arid to Hyperarid
## 11080 639
## Arid to Semi-arid Cold to Cold
## 340 95818
## Cold to Humid Dry subhumid to Dry subhumid
## 4393 4022
## Dry subhumid to Humid Dry subhumid to Semi-arid
## 288 2242
## Humid to Cold Humid to Dry subhumid
## 148 2484
## Humid to Humid Humid to Semi-arid
## 52925 198
## Hyperarid to Arid Hyperarid to Hyperarid
## 240 6402
## NA to NA Semi-arid to Arid
## 5130 1209
## Semi-arid to Dry subhumid Semi-arid to Semi-arid
## 277 12514
cmip6s$change.cat.mean <- factor(cmip6s$change.cat.mean,
levels = c("Hyperarid to Arid",
"Arid to Semi-arid",
"Semi-arid to Dry subhumid",
"Dry subhumid to Humid",
"Humid to Cold",
"Cold to Humid",
"Humid to Dry subhumid",
"Humid to Semi-arid",
"Dry subhumid to Semi-arid",
"Semi-arid to Arid",
"Arid to Hyperarid"))
wtd <- colorRampPalette(c("#FEE08B","firebrick"))
col.wtd <- wtd(6)
dtw <- colorRampPalette(c( "#DAE2F7","#4575B4" ))
col.dtw <- dtw(5)
colors_changes <- c("Hyperarid to Arid" = col.dtw[1],
"Arid to Semi-arid" = col.dtw[2],
"Semi-arid to Dry subhumid" = col.dtw[3],
"Dry subhumid to Humid" = col.dtw[4],
"Humid to Cold" = col.dtw[5],
"Cold to Humid" = col.wtd[1],
"Humid to Dry subhumid" = col.wtd[2],
"Humid to Semi-arid" = col.wtd[3],
"Dry subhumid to Semi-arid" = col.wtd[4],
"Semi-arid to Arid" = col.wtd[5],
"Arid to Hyperarid" = col.wtd[6])
8.2 Changes towards dryer categories cat mean
8.2.1 Historical
map_list <- list()
for(i in c("1850_1880","1985_2015")){
g <- ggplot() +
geom_raster(data = subset(cmip6s, period == i & model == "historical" & change.catmean == 1 & cat.dryer == "dryer"),
aes(x=lon, y = lat, fill = change.cat.mean))+
borders(colour = "grey60")+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(title = i, fill = "Change of category\ncompared to\n1970-2000")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 2, common.legend = T, legend = "bottom")
8.2.2 Future
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i & change.catmean == 1 & cat.dryer == "dryer"),
aes(x=lon, y = lat, fill = change.cat.mean))+
borders(colour = "grey60")+
scale_fill_manual(values = colors_changes)+
labs(fill = "Change of category\ncompared to\n1970-2000", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[index]] <- g
}}
ggarrange(plotlist = map_list, ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
8.3 Changes towards wetter categories cat mean
8.3.1 Historical
map_list <- list()
for(i in c("1850_1880","1985_2015")){
g <- ggplot() + geom_tile(data = subset(cmip6s, period == i & model == "historical" & change.catmaj == 1 & cat.dryer == "wetter"),
aes(x=lon, y = lat, fill = change.cat.mean))+
borders(colour = "grey60", ylim = c(-40,40))+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(title = i, fill = "Change of category\ncompared to\n1970-2000")+
theme_void()+ylim(-50,50)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[i]] <- g
}
ggarrange(plotlist = map_list, ncol = 2, common.legend = T, legend = "bottom")
8.3.2 Future
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i & change.catmean == 1 & cat.dryer == "wetter"),
aes(x=lon, y = lat, fill = change.cat.mean))+
borders(colour = "grey60", ylim = c(-40,40))+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(fill = "Change of category\ncompared to\n1970-2000", title = index)+
theme_void()+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 2))
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
8.4 Big map of SSP585 2070-2100
g <- ggplot() +
geom_tile(data = subset(cmip6s, period == "2070_2100" & model == "SSP585" & change.catmean == 1), aes(x=lon, y = lat, fill = change.cat.mean))+
borders(colour = "grey60")+
scale_fill_manual(values = colors_changes, na.translate = F)+
labs(fill = "Change of aridity category\nfor the period 2070-2100\nand SSP 5-8.5,\ncompared to 1970-2000", title = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")+guides(fill = guide_legend(nrow = 4))
print(g)
9 Tables
9.1 Changes of aridity category using cat mean
tab <- cmip6s %>% group_by(model, period, Continent) %>% summarise(Cold = table(cat.AI)[1], Hyperarid = table(cat.AI)[2], Arid = table(cat.AI)[3], Semiarid = table(cat.AI)[4], Drysubhumid = table(cat.AI)[5], Humid = table(cat.AI)[6], total = n())
write.table(tab, "tab.catAI.txt")
tab.future <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(period, model, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(period, model) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(period + model ~cat.AI) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
kable(tab.future[order(tab.future$model, decreasing = F),]) %>% kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(7,10), italic = T, include_thead = T) %>% row_spec(2, bold = T) | model | period | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 3.4 | 6.0 | 7.1 | 3.3 | 19.8 | 28.8 | 48.9 | 2.6 |
| 2 | historical | 1970_2000 | 3.3 | 6.0 | 7.0 | 3.3 | 19.6 | 27.8 | 49.9 | 2.6 |
| 3 | historical | 1985_2015 | 3.3 | 6.1 | 6.9 | 3.3 | 19.6 | 27.3 | 50.6 | 2.6 |
| 4 | SSP245 | 2030_2060 | 3.5 | 6.3 | 7.8 | 3.3 | 20.9 | 28.8 | 47.7 | 2.6 |
| 7 | SSP245 | 2070_2100 | 3.6 | 6.4 | 7.8 | 3.5 | 21.3 | 29.4 | 46.6 | 2.6 |
| 5 | SSP370 | 2030_2060 | 3.2 | 6.7 | 7.6 | 3.0 | 20.5 | 28.8 | 48.1 | 2.6 |
| 8 | SSP370 | 2070_2100 | 4.0 | 6.3 | 8.3 | 3.7 | 22.3 | 28.5 | 46.6 | 2.6 |
| 6 | SSP585 | 2030_2060 | 3.6 | 6.2 | 7.9 | 3.5 | 21.2 | 29.3 | 47.0 | 2.6 |
| 9 | SSP585 | 2070_2100 | 3.8 | 6.4 | 8.4 | 3.6 | 22.2 | 30.1 | 45.1 | 2.6 |
# ggplot(tab.future, aes(x = period))+
# geom_point(aes(y = Hyperarid, col = "Hyperarid", shape = model))+
# geom_line(aes(y=Hyperarid, col = "Hyperarid", group = model, lty = model))+
# geom_point(aes(y = Arid, col = "Arid", shape = model))+
# geom_line(aes(y=Arid, col = "Arid", group = model, lty = model))+
# geom_point(aes(y = get('Semi-arid'), col = "Semi-arid", shape = model))+
# geom_line(aes(y=get('Semi-arid'), col = "Semi-arid", group = model, lty = model))+
# geom_point(aes(y = get('Dry subhumid'), col = "Dry subhumid", shape = model))+
# geom_line(aes(y=get('Dry subhumid'), col = "Dry subhumid", group = model, lty = model))+
# geom_point(aes(y = Humid, col = "Humid", shape = model))+
# geom_line(aes(y= Humid, col = "Humid", group = model, lty = model))+
# scale_color_manual(values = col.cat)+
# theme_minimal()
cmip6$cat.AI <- factor(cmip6$cat.AI, levels = c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid", "Cold"))
tab.percent <- cmip6 %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(period, model, cat.AI, source) %>%
summarise(count = n()) %>%
ungroup() %>%
group_by(period, model, cat.AI) %>%
summarise(mmmean = mean(count, na.rm = T), mmsd = sd(count, na.rm = T)) %>%
mutate(percent = round(mmmean/sum(mmmean)*100, 1), sd.percent = round(mmsd/sum(mmmean)*100,1))
write.table(tab.percent, "tab.percent.txt")tab.percent <- read.table("tab.percent.txt")
df245 <- rbind(subset(tab.percent, model == "historical" & period %in% c("1970_2000")), subset(tab.percent, model == "SSP245")) %>%
group_by(period, model) %>%
mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.AI %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
df245$cat.AI <- factor(df245$cat.AI, levels = c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid", "Cold"))
b245 <- ggplot(data = df245, aes(x = period, y = percent))+
geom_col(aes(group = cat.AI, col = cat.AI, fill = cat.AI))+
geom_label(aes(y = lab.y, label = paste(percent, " ± ", sd.percent, sep = "")), size = 3)+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
labs(title = "SSP 2-4.5", y = "%", x = "", col = "", fill = "")+
theme_minimal()
df370 <- rbind(subset(tab.percent, model == "historical" & period %in% c( "1970_2000")), subset(tab.percent, model == "SSP370")) %>%
group_by(period, model) %>%
mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.AI %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
df370$cat.AI <- factor(df370$cat.AI, levels = c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid", "Cold"))
b370 <- ggplot(data = df370, aes(x = period, y = percent))+
geom_col(aes(group = cat.AI, col = cat.AI, fill = cat.AI))+
geom_label(aes(y = lab.y, label = paste(percent, " ± ", sd.percent, sep = "")), size = 3)+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
labs(title = "SSP 3-7.0", y = "%", x = "", col = "", fill = "")+
theme_minimal()
df585 <- rbind(subset(tab.percent, model == "historical" & period %in% c("1970_2000")), subset(tab.percent, model == "SSP585")) %>%
group_by(period, model) %>%
mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.AI %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
df585$cat.AI <- factor(df585$cat.AI, levels = c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid", "Cold"))
b585 <- ggplot(data = df585, aes(x = period, y = percent))+
geom_col(aes(group = cat.AI, col = cat.AI, fill = cat.AI))+
geom_label(aes(y = lab.y, label = paste(percent, " ± ", sd.percent, sep = "")), size = 3)+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
labs(title = "SSP 5-8.5", y = "%", x = "", col = "", fill = "")+
theme_minimal()
ggarrange(plotlist = list(b245, b370, b585), common.legend = T, legend = "bottom", ncol = 3)
9.2 Changes of aridity category using cat maj
tab.maj <- cmip6s %>% group_by(model, period, Continent) %>% summarise(Cold = table(cat.maj)[1], Hyperarid = table(cat.maj)[2], Arid = table(cat.maj)[3], Semiarid = table(cat.maj)[4], Drysubhumid = table(cat.maj)[5], Humid = table(cat.maj)[6], total = n())
write.table(tab.maj, "tab.catmaj.txt")
tab.future.maj <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC")) %>%
group_by(period, model, cat.maj) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(period, model) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL) %>%
reshape2::dcast(period + model ~cat.maj) %>%
mutate("Sum drylands" = rowSums(.[c("Hyperarid","Arid","Semi-arid","Dry subhumid")])) %>%
select(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
kable(tab.future.maj[order(tab.future.maj$model, decreasing = F),]) %>% kable_styling(bootstrap_options = "bordered") %>%
column_spec(c(7,10), italic = T, include_thead = T) %>% row_spec(2, bold = T) | model | period | Hyperarid | Arid | Semi-arid | Dry subhumid | Sum drylands | Humid | Cold | NA | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 4.2 | 7.1 | 8.7 | 0.8 | 20.8 | 28.3 | 48.3 | 2.6 |
| 2 | historical | 1970_2000 | 4.1 | 7.1 | 8.6 | 0.7 | 20.5 | 28.1 | 48.9 | 2.6 |
| 3 | historical | 1985_2015 | 4.0 | 7.0 | 8.4 | 0.9 | 20.3 | 27.3 | 49.9 | 2.6 |
| 4 | SSP245 | 2030_2060 | 4.0 | 7.8 | 9.3 | 0.8 | 21.9 | 28.4 | 47.2 | 2.6 |
| 7 | SSP245 | 2070_2100 | 4.2 | 7.7 | 9.4 | 0.9 | 22.2 | 29.1 | 46.1 | 2.6 |
| 5 | SSP370 | 2030_2060 | 4.1 | 7.2 | 9.4 | 0.8 | 21.5 | 28.6 | 47.3 | 2.6 |
| 8 | SSP370 | 2070_2100 | 4.5 | 7.7 | 10.0 | 0.9 | 23.1 | 28.4 | 45.9 | 2.6 |
| 6 | SSP585 | 2030_2060 | 4.2 | 7.5 | 9.6 | 1.0 | 22.3 | 28.8 | 46.3 | 2.6 |
| 9 | SSP585 | 2070_2100 | 4.5 | 7.9 | 9.4 | 0.9 | 22.7 | 30.0 | 44.6 | 2.6 |
tab.flow.catmaj <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(period, model, cat.maj) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(period, model) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL)
df245 <- rbind(subset(tab.flow.catmaj, model == "historical" & period %in% c("1970_2000")), subset(tab.flow.catmaj, model == "SSP245")) %>%
mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.maj %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
b245 <- ggplot(data = df245, aes(x = period, y = percent))+
geom_col(aes(group = cat.maj, col = cat.maj, fill = cat.maj))+
geom_label(aes(y = lab.y, label = percent))+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
labs(title = "SSP 2-4.5", y = "%", x = "")+
theme_minimal()
df370 <- rbind(subset(tab.flow.catmaj, model == "historical" & period %in% c("1970_2000")), subset(tab.flow.catmaj, model == "SSP370")) %>% mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.maj %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
b370 <- ggplot(data = df370, aes(x = period, y = percent))+
geom_col(aes(group = cat.maj, col = cat.maj, fill = cat.maj))+
geom_label(aes(y = lab.y, label = percent))+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
labs(title = "SSP 3-7.0", y = "%", x = "")+
theme_minimal()
df585 <- rbind(subset(tab.flow.catmaj, model == "historical" & period %in% c("1970_2000")), subset(tab.flow.catmaj, model == "SSP585")) %>% mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.maj %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
b585 <- ggplot(data = df585, aes(x = period, y = percent))+
geom_col(aes(group = cat.maj, col = cat.maj, fill = cat.maj))+
geom_label(aes(y = lab.y, label = percent))+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
labs(title = "SSP 5-8.5", y = "%", x = "")+
theme_minimal()
ggarrange(plotlist = list(b245, b370, b585), common.legend = T, legend = "bottom", ncol = 3)
9.3 Changes by continent, cat mean
cmip6$cat.AI <- factor(cmip6$cat.AI, levels = c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid", "Cold"))
tab.percent.cont <- cmip6 %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(period, model, Continent, cat.AI, source) %>%
summarise(count = n()) %>%
ungroup() %>%
group_by(period, model, Continent, cat.AI) %>%
summarise(mmmean = mean(count, na.rm = T), mmsd = sd(count, na.rm = T)) %>%
mutate(percent = round(mmmean/sum(mmmean)*100, 1), sd.percent = round(mmsd/sum(mmmean)*100,1))
write.table(tab.percent.cont, "tab.percent.continent.txt")tab.percent.cont <- read.table("tab.percent.continent.txt")
tab_list_percent <- list()
tab_list_sd <- list()
for(i in unique(tab.percent.cont$Continent)){
tab.i <- subset(tab.percent.cont, Continent == i)
df.percent <- tab.i %>% reshape2::dcast(period + model ~ cat.AI, value.var = "percent")
df.sd <- tab.i %>% reshape2::dcast(period + model ~ cat.AI, value.var = "sd.percent")
# sd for a sum: square root of sum of squared sd
drycats <- which(names(df.percent) %in% c("Hyperarid","Arid","Semi-arid","Dry subhumid"))
df.percent <- df.percent %>% mutate("Sum drylands" = rowSums(.[drycats], na.rm = T))
df.sd <- df.sd %>% mutate("Sum drylands" = round(sqrt(rowSums((.[drycats])^2, na.rm = T)),2))
missing <- setdiff(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"), names(df.percent))
df.percent[, missing] <- 0
df.sd[,missing] <- 0
df.percent <- df.percent %>% select(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
df.percent[is.na(df.percent)] <- 0
tab_list_percent[[i]] <- df.percent
df.sd <- df.sd %>% select(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
df.sd[is.na(df.sd)] <- 0
tab_list_sd[[i]] <- df.sd
}
k_list <- list()
kdiff_list <- list()
for(i in names(tab_list_percent)){
df <- tab_list_percent[[i]] %>% select(c("model", "period"))
prop.ref <- tab_list_percent[[i]] %>% filter(period == "1970_2000")
prop.ref <- prop.ref %>% rbind(prop.ref[rep(1,dim(df)[1]-1),])
df2 <- tab_list_percent[[i]][,c(3:10)] - prop.ref[,c(3:10)] %>% round(1)
diff.prop.percent <- cbind(df, df2)
sd.ref <- tab_list_sd[[i]] %>% filter(period == "1970_2000")
sd.ref <- sd.ref %>% rbind(sd.ref[rep(1,dim(df)[1]-1),])
df3 <- sqrt(tab_list_sd[[i]][,c(3:10)]^2 + sd.ref[,c(3:10)]^2) %>% round(1)
diff.sd.percent <- cbind(df, df3)
df$Hyperarid <- paste(tab_list_percent[[i]]$Hyperarid, tab_list_sd[[i]]$Hyperarid, sep = " ± ")
df$Arid <- paste(tab_list_percent[[i]]$Arid, tab_list_sd[[i]]$Arid, sep = " ± ")
df$'Semi-Arid' <- paste(tab_list_percent[[i]][,5], tab_list_sd[[i]][,5], sep = " ± ")
df$'Dry subhumid' <- paste(tab_list_percent[[i]][,6], tab_list_sd[[i]][,6], sep = " ± ")
df$'Sum drylands' <- paste(tab_list_percent[[i]][,7], tab_list_sd[[i]][,7], sep = " ± ")
df$Humid <- paste(tab_list_percent[[i]]$Humid, tab_list_sd[[i]]$Humid, sep = " ± ")
df$Cold <- paste(tab_list_percent[[i]]$Cold, tab_list_sd[[i]]$Cold, sep = " ± ")
df.diff <- df
df.diff$Hyperarid <- paste(round(diff.prop.percent$Hyperarid, 1), round(diff.sd.percent$Hyperarid, 1), sep = " ± ")
df.diff$Arid <- paste(round(diff.prop.percent$Arid, 1), round(diff.sd.percent$Arid, 1), sep = " ± ")
df.diff$'Semi-Arid' <- paste(round(diff.prop.percent[,5], 1), round(diff.sd.percent[,5], 1), sep = " ± ")
df.diff$'Dry subhumid' <- paste(round(diff.prop.percent[,6], 1), round(diff.sd.percent[,6], 1), sep = " ± ")
df.diff$'Sum drylands' <- paste(round(diff.prop.percent[,7],1), round(diff.sd.percent[,7], 1), sep = " ± ")
df.diff$Humid <- paste(round(diff.prop.percent$Humid, 1), round(diff.sd.percent$Humid, 1), sep = " ± ")
df.diff$Cold <- paste(round(diff.prop.percent$Cold, 1), round(diff.sd.percent$Cold, 1), sep = " ± ")
k_list[[i]] <- kable(df[order(df$model, decreasing = F),], caption = i) %>% kable_styling(bootstrap_options = "bordered") %>%
column_spec(8, italic = T, background = "#DAE2F7") %>% row_spec(2, bold = T)
kdiff_list[[i]] <- kable(df.diff[order(df.diff$model, decreasing = F),], caption = i) %>% kable_styling(bootstrap_options = "bordered") %>%
column_spec(8, italic = T, background = "#DAE2F7") %>% row_spec(2, bold = T)
}9.3.1 Proportion of aridity categories by continent
k_list| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 27.2 ± 4.7 | 17.1 ± 4.9 | 15.6 ± 3.6 | 6.3 ± 1.4 | 66.2 ± 7.81 | 33.7 ± 7.1 | 0 ± 0 |
| 2 | historical | 1970_2000 | 26.9 ± 4.7 | 16.7 ± 4.2 | 15.1 ± 3.4 | 6.2 ± 1.5 | 64.9 ± 7.32 | 32.8 ± 6.2 | 2.3 ± 0 |
| 3 | historical | 1985_2015 | 26.5 ± 4.8 | 17.1 ± 4.6 | 15.4 ± 3.3 | 6 ± 1.5 | 65 ± 7.57 | 32.7 ± 6.3 | 2.2 ± 0 |
| 4 | SSP245 | 2030_2060 | 27 ± 4.8 | 18 ± 4.9 | 16.5 ± 3.5 | 6.4 ± 1.7 | 67.9 ± 7.89 | 32 ± 7.6 | 0 ± 0 |
| 7 | SSP245 | 2070_2100 | 27.9 ± 4.7 | 17.4 ± 5 | 17.2 ± 3.4 | 6.2 ± 1.5 | 68.7 ± 7.8 | 31.3 ± 7.9 | 0 ± 0 |
| 5 | SSP370 | 2030_2060 | 26 ± 5 | 16.6 ± 3.6 | 17.2 ± 1.8 | 6.8 ± 1 | 66.6 ± 6.5 | 32.3 ± 6.7 | 1.1 ± 0 |
| 8 | SSP370 | 2070_2100 | 28 ± 3.5 | 17.6 ± 4.8 | 16.6 ± 3.1 | 6.2 ± 1.6 | 68.4 ± 6.89 | 30.4 ± 6.4 | 1.2 ± 0 |
| 6 | SSP585 | 2030_2060 | 27.1 ± 4.6 | 17.6 ± 4.8 | 16.8 ± 3.5 | 6.3 ± 1.5 | 67.8 ± 7.66 | 32.1 ± 7.5 | 0 ± 0 |
| 9 | SSP585 | 2070_2100 | 29.7 ± 10.5 | 17.3 ± 5.3 | 16.2 ± 4.2 | 6 ± 1.5 | 69.2 ± 12.58 | 30.9 ± 8.6 | 0 ± 0 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 3.9 ± 2.3 | 12.2 ± 1.9 | 9.2 ± 1.5 | 4 ± 1.4 | 29.3 ± 3.62 | 32.4 ± 7.7 | 38 ± 9.2 |
| 2 | historical | 1970_2000 | 3.8 ± 2.1 | 12 ± 1.8 | 8.9 ± 1.5 | 3.8 ± 1.1 | 28.5 ± 3.33 | 30.7 ± 8.5 | 40.5 ± 9.6 |
| 3 | historical | 1985_2015 | 3.7 ± 2.2 | 11.8 ± 1.6 | 8.6 ± 1.5 | 3.7 ± 1.1 | 27.8 ± 3.3 | 30.1 ± 7.6 | 41.8 ± 8.8 |
| 4 | SSP245 | 2030_2060 | 3.5 ± 1.8 | 12.5 ± 1.6 | 9.4 ± 1.6 | 4.3 ± 1.7 | 29.7 ± 3.35 | 34.3 ± 8.1 | 35.8 ± 9.2 |
| 7 | SSP245 | 2070_2100 | 3.7 ± 2 | 12.5 ± 1.7 | 9.5 ± 1.7 | 4.2 ± 1.7 | 29.9 ± 3.56 | 36.2 ± 8.6 | 33.7 ± 9.3 |
| 5 | SSP370 | 2030_2060 | 3.4 ± 2.3 | 12.4 ± 1.5 | 8.9 ± 1.7 | 3.7 ± 1.3 | 28.4 ± 3.48 | 34 ± 7.2 | 37.3 ± 9.5 |
| 8 | SSP370 | 2070_2100 | 4.6 ± 1.9 | 12.5 ± 1.5 | 9.6 ± 1.5 | 3.8 ± 1.2 | 30.5 ± 3.09 | 35.2 ± 9.2 | 34.1 ± 9.7 |
| 6 | SSP585 | 2030_2060 | 3.8 ± 2 | 12.5 ± 1.6 | 9.5 ± 1.8 | 4.2 ± 1.6 | 30 ± 3.52 | 35.4 ± 8.4 | 34.4 ± 9.3 |
| 9 | SSP585 | 2070_2100 | 4.8 ± 3.2 | 13.1 ± 3.2 | 10.4 ± 3.4 | 4.2 ± 1.5 | 32.5 ± 5.86 | 36.5 ± 11.7 | 30.7 ± 9.9 |
CENTRAL-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 8.4 ± 3.6 | 30.1 ± 5.1 | 16.1 ± 8.7 | 54.6 ± 10.71 | 45 ± 6.1 | 0 ± 0 |
| 2 | historical | 1970_2000 | 0 ± 0 | 8.4 ± 4.1 | 29.7 ± 7.6 | 14.7 ± 7.8 | 52.8 ± 11.64 | 44.9 ± 12 | 2 ± 0 |
| 3 | historical | 1985_2015 | 0 ± 0 | 9 ± 4.2 | 30.7 ± 7 | 14.8 ± 7.4 | 54.5 ± 11.02 | 43.8 ± 11.5 | 1.4 ± 0 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 11.9 ± 6.2 | 36.3 ± 8 | 13.9 ± 6.2 | 62.1 ± 11.87 | 37.6 ± 8.6 | 0 ± 0 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 12.3 ± 5.5 | 38.1 ± 8.5 | 14.4 ± 7.9 | 64.8 ± 12.84 | 34.8 ± 8.6 | 0 ± 0 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 12.6 ± 6.3 | 31.4 ± 7.1 | 15 ± 7.9 | 59 ± 12.35 | 40.7 ± 11.8 | 0 ± 0 |
| 8 | SSP370 | 2070_2100 | 4 ± 5.2 | 16 ± 5.5 | 35.5 ± 9.5 | 13.2 ± 5.2 | 68.7 ± 13.21 | 30.9 ± 10.6 | 0 ± 0 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 13 ± 7 | 37.6 ± 9 | 14.1 ± 7.1 | 64.7 ± 13.43 | 35 ± 9.4 | 0 ± 0 |
| 9 | SSP585 | 2070_2100 | 2 ± 2.4 | 16.6 ± 8 | 41.2 ± 7.7 | 13.3 ± 7.7 | 73.1 ± 13.72 | 26.6 ± 8.8 | 0 ± 0 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 1.9 ± 1.2 | 9.6 ± 4.1 | 4.5 ± 2.8 | 16 ± 5.11 | 49 ± 15.3 | 34.8 ± 20.2 |
| 2 | historical | 1970_2000 | 0 ± 0 | 1.5 ± 1 | 8.9 ± 3.4 | 3.9 ± 2.1 | 14.3 ± 4.12 | 44.9 ± 16.1 | 40.6 ± 20.4 |
| 3 | historical | 1985_2015 | 0 ± 0 | 1.3 ± 1 | 8.4 ± 2.9 | 3.8 ± 2 | 13.5 ± 3.66 | 46 ± 16.1 | 40.3 ± 19.9 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 1.9 ± 0.9 | 10.3 ± 3.6 | 5.1 ± 1.9 | 17.3 ± 4.17 | 52.7 ± 15.4 | 29.8 ± 19.7 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 2.2 ± 1.1 | 10.1 ± 4.8 | 5.1 ± 2.3 | 17.4 ± 5.44 | 55.4 ± 14.4 | 27 ± 18.6 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 2.1 ± 1 | 9.7 ± 4.7 | 4.8 ± 2.5 | 16.6 ± 5.42 | 52.7 ± 15.7 | 30.4 ± 20 |
| 8 | SSP370 | 2070_2100 | 0 ± 0 | 3.1 ± 1.1 | 11.4 ± 5.9 | 6.4 ± 3.8 | 20.9 ± 7.1 | 52.8 ± 14.3 | 26 ± 18.6 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 2.3 ± 1.1 | 10.5 ± 3.7 | 4.9 ± 2.4 | 17.7 ± 4.55 | 55.2 ± 15.8 | 26.9 ± 19.9 |
| 9 | SSP585 | 2070_2100 | 0 ± 0 | 3 ± 1.7 | 11.1 ± 5.4 | 6.1 ± 2.9 | 20.2 ± 6.36 | 57.2 ± 12.8 | 22.5 ± 17.3 |
EUROPE-AFRICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 5.9 ± 4.9 | 23.6 ± 7 | 21.3 ± 8.8 | 8.8 ± 3.3 | 59.6 ± 12.7 | 24.9 ± 14.2 | 14.5 ± 0 |
| 2 | historical | 1970_2000 | 7 ± 5.5 | 26.5 ± 7.5 | 23.3 ± 10.3 | 9.9 ± 3.2 | 66.7 ± 14.24 | 27.5 ± 16.5 | 4.5 ± 7.5 |
| 3 | historical | 1985_2015 | 7.1 ± 5.9 | 26.3 ± 7.1 | 22.2 ± 7.7 | 10.3 ± 4.2 | 65.9 ± 12.73 | 27.8 ± 14.3 | 5.2 ± 7 |
| 4 | SSP245 | 2030_2060 | 9.9 ± 6.6 | 28.9 ± 8.6 | 26.7 ± 8.9 | 9.4 ± 3.4 | 74.9 ± 14.43 | 23.1 ± 16.2 | 0.9 ± 0 |
| 7 | SSP245 | 2070_2100 | 11.7 ± 5.7 | 28.6 ± 8.8 | 27.2 ± 8.1 | 9.5 ± 3.1 | 77 ± 13.61 | 21.9 ± 13.9 | 0 ± 0 |
| 5 | SSP370 | 2030_2060 | 10.3 ± 6.3 | 28.2 ± 8.4 | 26 ± 8.7 | 9 ± 2.8 | 73.5 ± 13.92 | 24.8 ± 14 | 0.5 ± 0 |
| 8 | SSP370 | 2070_2100 | 13.2 ± 6.2 | 27.3 ± 8.6 | 28.3 ± 9.7 | 8.7 ± 3.6 | 77.5 ± 14.81 | 21.2 ± 16.2 | 0 ± 0 |
| 6 | SSP585 | 2030_2060 | 11.1 ± 6.4 | 28.8 ± 8.3 | 27.5 ± 8.8 | 8.7 ± 3 | 76.1 ± 14.01 | 22.6 ± 14.1 | 0.2 ± 0 |
| 9 | SSP585 | 2070_2100 | 15.2 ± 5.7 | 29.1 ± 10.1 | 27.9 ± 8.8 | 8 ± 2.8 | 80.2 ± 14.82 | 18.6 ± 13.6 | 0 ± 0 |
NORTH-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 0.4 ± 0.2 | 5.8 ± 5.6 | 4.3 ± 2.6 | 10.5 ± 6.18 | 35.1 ± 9 | 54.2 ± 10.9 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0.4 ± 0.2 | 4.3 ± 4.4 | 4.2 ± 2.4 | 8.9 ± 5.02 | 34.5 ± 9.8 | 56.5 ± 11.7 |
| 3 | historical | 1985_2015 | 0 ± 0 | 0.3 ± 0.2 | 4.6 ± 3.8 | 4.1 ± 2.2 | 9 ± 4.4 | 33.6 ± 10.3 | 57.2 ± 11.5 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 0.4 ± 0.3 | 6.5 ± 5.5 | 4.7 ± 2.6 | 11.6 ± 6.09 | 37.5 ± 8.7 | 50.8 ± 11.1 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 0.5 ± 0.3 | 6.4 ± 5.1 | 5.1 ± 2.9 | 12 ± 5.87 | 39.7 ± 9 | 48.1 ± 11 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 0.5 ± 0.4 | 5.9 ± 4.8 | 4.6 ± 2.7 | 11 ± 5.52 | 37 ± 9.7 | 51.8 ± 12.2 |
| 8 | SSP370 | 2070_2100 | 0.1 ± 0 | 0.8 ± 0.7 | 7.6 ± 4 | 5.2 ± 2 | 13.7 ± 4.53 | 38.4 ± 10.6 | 47.7 ± 11.2 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 0.5 ± 0.4 | 6.6 ± 5.2 | 5.2 ± 2.8 | 12.3 ± 5.92 | 38.2 ± 9.1 | 49.3 ± 11.7 |
| 9 | SSP585 | 2070_2100 | 0.2 ± 0 | 1 ± 1.4 | 7 ± 6.1 | 5.2 ± 2.5 | 13.4 ± 6.74 | 42.1 ± 9.3 | 44.5 ± 11.3 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 5.3 ± 0 | 32 ± 26 | 36.7 ± 17.1 | 6.6 ± 3.1 | 80.6 ± 31.27 | 18.9 ± 12.5 | 0.3 ± 0.2 |
| 2 | historical | 1970_2000 | 5.2 ± 0 | 31.3 ± 26.4 | 36.8 ± 17.4 | 6.8 ± 3.1 | 80.1 ± 31.77 | 19.3 ± 12.8 | 0.5 ± 0.4 |
| 3 | historical | 1985_2015 | 5.2 ± 0 | 33.4 ± 26.1 | 35.5 ± 17.9 | 6.8 ± 3.2 | 80.9 ± 31.81 | 18.4 ± 12.3 | 0.6 ± 0.3 |
| 4 | SSP245 | 2030_2060 | 10.4 ± 0 | 35.8 ± 23.5 | 31.7 ± 15.1 | 5.7 ± 2.6 | 83.6 ± 28.05 | 15.9 ± 11.9 | 0.3 ± 0.3 |
| 7 | SSP245 | 2070_2100 | 10.5 ± 0 | 37.9 ± 22.2 | 29.1 ± 13.1 | 6.2 ± 3.3 | 83.7 ± 25.99 | 16.2 ± 12 | 0.1 ± 0 |
| 5 | SSP370 | 2030_2060 | 10.7 ± 0 | 34.8 ± 22.6 | 33.4 ± 15.3 | 5.8 ± 2.5 | 84.7 ± 27.41 | 14.9 ± 8.1 | 0.3 ± 0.2 |
| 8 | SSP370 | 2070_2100 | 10.5 ± 10.2 | 38.1 ± 19.7 | 28.5 ± 13.8 | 5.7 ± 3.2 | 82.8 ± 26.32 | 16.8 ± 16.3 | 0.3 ± 0.3 |
| 6 | SSP585 | 2030_2060 | 5.6 ± 3.6 | 35.6 ± 25.3 | 33.8 ± 16.7 | 6.7 ± 3.6 | 81.7 ± 30.74 | 17.7 ± 13.8 | 0.3 ± 0.3 |
| 9 | SSP585 | 2070_2100 | 9.3 ± 6.4 | 39.4 ± 21.7 | 29.4 ± 12.9 | 5.5 ± 2.5 | 83.6 ± 26.16 | 16.2 ± 12.5 | 0 ± 0 |
SOUTH-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0.1 ± 0.1 | 2.1 ± 1.7 | 15.2 ± 8.7 | 7.4 ± 2.6 | 24.8 ± 9.24 | 73.2 ± 11.7 | 1.9 ± 1 |
| 2 | historical | 1970_2000 | 0.1 ± 0.1 | 1.8 ± 1.3 | 13.9 ± 4.3 | 7.3 ± 2.3 | 23.1 ± 5.05 | 74.5 ± 7.3 | 2.3 ± 2.6 |
| 3 | historical | 1985_2015 | 0.2 ± 0.1 | 2 ± 1.2 | 14.5 ± 5.1 | 7.5 ± 2.4 | 24.2 ± 5.76 | 73.6 ± 7.7 | 2.1 ± 2.6 |
| 4 | SSP245 | 2030_2060 | 0.2 ± 0.2 | 2.7 ± 2.2 | 17.4 ± 9.6 | 8.8 ± 3.6 | 29.1 ± 10.49 | 69.4 ± 13.1 | 1.3 ± 0.9 |
| 7 | SSP245 | 2070_2100 | 0.2 ± 0.1 | 3.3 ± 3.5 | 18.3 ± 9.1 | 9.3 ± 4.6 | 31.1 ± 10.78 | 67.5 ± 14.7 | 1.3 ± 0.7 |
| 5 | SSP370 | 2030_2060 | 0.2 ± 0.1 | 2.2 ± 1.7 | 16.3 ± 5.5 | 8.8 ± 3.7 | 27.5 ± 6.84 | 70.5 ± 8.9 | 1.9 ± 1.6 |
| 8 | SSP370 | 2070_2100 | 0.2 ± 0.1 | 3.3 ± 2.5 | 18.6 ± 7 | 8.7 ± 3.8 | 30.8 ± 8.35 | 67.3 ± 10.8 | 1.8 ± 1.8 |
| 6 | SSP585 | 2030_2060 | 0.2 ± 0.1 | 3.1 ± 2.5 | 18.6 ± 9 | 9.2 ± 4.4 | 31.1 ± 10.33 | 67.6 ± 13.1 | 1.2 ± 0.8 |
| 9 | SSP585 | 2070_2100 | 0.6 ± 1 | 4.4 ± 3.8 | 19.6 ± 9.5 | 9.7 ± 3.9 | 34.3 ± 11 | 64.4 ± 12.6 | 1.1 ± 0.7 |
9.3.2 Changes in proportion of aridity categories by continent
kdiff_list| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0.3 ± 6.6 | 0.4 ± 6.5 | 0.5 ± 5 | 0.1 ± 2.1 | 1.3 ± 10.7 | 0.9 ± 9.4 | -2.3 ± 0 |
| 2 | historical | 1970_2000 | 0 ± 6.6 | 0 ± 5.9 | 0 ± 4.8 | 0 ± 2.1 | 0 ± 10.4 | 0 ± 8.8 | 0 ± 0 |
| 3 | historical | 1985_2015 | -0.4 ± 6.7 | 0.4 ± 6.2 | 0.3 ± 4.7 | -0.2 ± 2.1 | 0.1 ± 10.5 | -0.1 ± 8.8 | -0.1 ± 0 |
| 4 | SSP245 | 2030_2060 | 0.1 ± 6.7 | 1.3 ± 6.5 | 1.4 ± 4.9 | 0.2 ± 2.3 | 3 ± 10.8 | -0.8 ± 9.8 | -2.3 ± 0 |
| 7 | SSP245 | 2070_2100 | 1 ± 6.6 | 0.7 ± 6.5 | 2.1 ± 4.8 | 0 ± 2.1 | 3.8 ± 10.7 | -1.5 ± 10 | -2.3 ± 0 |
| 5 | SSP370 | 2030_2060 | -0.9 ± 6.9 | -0.1 ± 5.5 | 2.1 ± 3.8 | 0.6 ± 1.8 | 1.7 ± 9.8 | -0.5 ± 9.1 | -1.2 ± 0 |
| 8 | SSP370 | 2070_2100 | 1.1 ± 5.9 | 0.9 ± 6.4 | 1.5 ± 4.6 | 0 ± 2.2 | 3.5 ± 10.1 | -2.4 ± 8.9 | -1.1 ± 0 |
| 6 | SSP585 | 2030_2060 | 0.2 ± 6.6 | 0.9 ± 6.4 | 1.7 ± 4.9 | 0.1 ± 2.1 | 2.9 ± 10.6 | -0.7 ± 9.7 | -2.3 ± 0 |
| 9 | SSP585 | 2070_2100 | 2.8 ± 11.5 | 0.6 ± 6.8 | 1.1 ± 5.4 | -0.2 ± 2.1 | 4.3 ± 14.6 | -1.9 ± 10.6 | -2.3 ± 0 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0.1 ± 3.1 | 0.2 ± 2.6 | 0.3 ± 2.1 | 0.2 ± 1.8 | 0.8 ± 4.9 | 1.7 ± 11.5 | -2.5 ± 13.3 |
| 2 | historical | 1970_2000 | 0 ± 3 | 0 ± 2.5 | 0 ± 2.1 | 0 ± 1.6 | 0 ± 4.7 | 0 ± 12 | 0 ± 13.6 |
| 3 | historical | 1985_2015 | -0.1 ± 3 | -0.2 ± 2.4 | -0.3 ± 2.1 | -0.1 ± 1.6 | -0.7 ± 4.7 | -0.6 ± 11.4 | 1.3 ± 13 |
| 4 | SSP245 | 2030_2060 | -0.3 ± 2.8 | 0.5 ± 2.4 | 0.5 ± 2.2 | 0.5 ± 2 | 1.2 ± 4.7 | 3.6 ± 11.7 | -4.7 ± 13.3 |
| 7 | SSP245 | 2070_2100 | -0.1 ± 2.9 | 0.5 ± 2.5 | 0.6 ± 2.3 | 0.4 ± 2 | 1.4 ± 4.9 | 5.5 ± 12.1 | -6.8 ± 13.4 |
| 5 | SSP370 | 2030_2060 | -0.4 ± 3.1 | 0.4 ± 2.3 | 0 ± 2.3 | -0.1 ± 1.7 | -0.1 ± 4.8 | 3.3 ± 11.1 | -3.2 ± 13.5 |
| 8 | SSP370 | 2070_2100 | 0.8 ± 2.8 | 0.5 ± 2.3 | 0.7 ± 2.1 | 0 ± 1.6 | 2 ± 4.5 | 4.5 ± 12.5 | -6.4 ± 13.6 |
| 6 | SSP585 | 2030_2060 | 0 ± 2.9 | 0.5 ± 2.4 | 0.6 ± 2.3 | 0.4 ± 1.9 | 1.5 ± 4.8 | 4.7 ± 12 | -6.1 ± 13.4 |
| 9 | SSP585 | 2070_2100 | 1 ± 3.8 | 1.1 ± 3.7 | 1.5 ± 3.7 | 0.4 ± 1.9 | 4 ± 6.7 | 5.8 ± 14.5 | -9.8 ± 13.8 |
CENTRAL-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 0 ± 5.5 | 0.4 ± 9.2 | 1.4 ± 11.7 | 1.8 ± 15.8 | 0.1 ± 13.5 | -2 ± 0 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 5.8 | 0 ± 10.7 | 0 ± 11 | 0 ± 16.5 | 0 ± 17 | 0 ± 0 |
| 3 | historical | 1985_2015 | 0 ± 0 | 0.6 ± 5.9 | 1 ± 10.3 | 0.1 ± 10.8 | 1.7 ± 16 | -1.1 ± 16.6 | -0.6 ± 0 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 3.5 ± 7.4 | 6.6 ± 11 | -0.8 ± 10 | 9.3 ± 16.6 | -7.3 ± 14.8 | -2 ± 0 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 3.9 ± 6.9 | 8.4 ± 11.4 | -0.3 ± 11.1 | 12 ± 17.3 | -10.1 ± 14.8 | -2 ± 0 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 4.2 ± 7.5 | 1.7 ± 10.4 | 0.3 ± 11.1 | 6.2 ± 17 | -4.2 ± 16.8 | -2 ± 0 |
| 8 | SSP370 | 2070_2100 | 4 ± 5.2 | 7.6 ± 6.9 | 5.8 ± 12.2 | -1.5 ± 9.4 | 15.9 ± 17.6 | -14 ± 16 | -2 ± 0 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 4.6 ± 8.1 | 7.9 ± 11.8 | -0.6 ± 10.5 | 11.9 ± 17.8 | -9.9 ± 15.2 | -2 ± 0 |
| 9 | SSP585 | 2070_2100 | 2 ± 2.4 | 8.2 ± 9 | 11.5 ± 10.8 | -1.4 ± 11 | 20.3 ± 18 | -18.3 ± 14.9 | -2 ± 0 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 0.4 ± 1.6 | 0.7 ± 5.3 | 0.6 ± 3.5 | 1.7 ± 6.6 | 4.1 ± 22.2 | -5.8 ± 28.7 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 1.4 | 0 ± 4.8 | 0 ± 3 | 0 ± 5.8 | 0 ± 22.8 | 0 ± 28.8 |
| 3 | historical | 1985_2015 | 0 ± 0 | -0.2 ± 1.4 | -0.5 ± 4.5 | -0.1 ± 2.9 | -0.8 ± 5.5 | 1.1 ± 22.8 | -0.3 ± 28.5 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 0.4 ± 1.3 | 1.4 ± 5 | 1.2 ± 2.8 | 3 ± 5.9 | 7.8 ± 22.3 | -10.8 ± 28.4 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 0.7 ± 1.5 | 1.2 ± 5.9 | 1.2 ± 3.1 | 3.1 ± 6.8 | 10.5 ± 21.6 | -13.6 ± 27.6 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 0.6 ± 1.4 | 0.8 ± 5.8 | 0.9 ± 3.3 | 2.3 ± 6.8 | 7.8 ± 22.5 | -10.2 ± 28.6 |
| 8 | SSP370 | 2070_2100 | 0 ± 0 | 1.6 ± 1.5 | 2.5 ± 6.8 | 2.5 ± 4.3 | 6.6 ± 8.2 | 7.9 ± 21.5 | -14.6 ± 27.6 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 0.8 ± 1.5 | 1.6 ± 5 | 1 ± 3.2 | 3.4 ± 6.1 | 10.3 ± 22.6 | -13.7 ± 28.5 |
| 9 | SSP585 | 2070_2100 | 0 ± 0 | 1.5 ± 2 | 2.2 ± 6.4 | 2.2 ± 3.6 | 5.9 ± 7.6 | 12.3 ± 20.6 | -18.1 ± 26.7 |
EUROPE-AFRICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | -1.1 ± 7.4 | -2.9 ± 10.3 | -2 ± 13.5 | -1.1 ± 4.6 | -7.1 ± 19.1 | -2.6 ± 21.8 | 10 ± 7.5 |
| 2 | historical | 1970_2000 | 0 ± 7.8 | 0 ± 10.6 | 0 ± 14.6 | 0 ± 4.5 | 0 ± 20.1 | 0 ± 23.3 | 0 ± 10.6 |
| 3 | historical | 1985_2015 | 0.1 ± 8.1 | -0.2 ± 10.3 | -1.1 ± 12.9 | 0.4 ± 5.3 | -0.8 ± 19.1 | 0.3 ± 21.8 | 0.7 ± 10.3 |
| 4 | SSP245 | 2030_2060 | 2.9 ± 8.6 | 2.4 ± 11.4 | 3.4 ± 13.6 | -0.5 ± 4.7 | 8.2 ± 20.3 | -4.4 ± 23.1 | -3.6 ± 7.5 |
| 7 | SSP245 | 2070_2100 | 4.7 ± 7.9 | 2.1 ± 11.6 | 3.9 ± 13.1 | -0.4 ± 4.5 | 10.3 ± 19.7 | -5.6 ± 21.6 | -4.5 ± 7.5 |
| 5 | SSP370 | 2030_2060 | 3.3 ± 8.4 | 1.7 ± 11.3 | 2.7 ± 13.5 | -0.9 ± 4.3 | 6.8 ± 19.9 | -2.7 ± 21.6 | -4 ± 7.5 |
| 8 | SSP370 | 2070_2100 | 6.2 ± 8.3 | 0.8 ± 11.4 | 5 ± 14.1 | -1.2 ± 4.8 | 10.8 ± 20.5 | -6.3 ± 23.1 | -4.5 ± 7.5 |
| 6 | SSP585 | 2030_2060 | 4.1 ± 8.4 | 2.3 ± 11.2 | 4.2 ± 13.5 | -1.2 ± 4.4 | 9.4 ± 20 | -4.9 ± 21.7 | -4.3 ± 7.5 |
| 9 | SSP585 | 2070_2100 | 8.2 ± 7.9 | 2.6 ± 12.6 | 4.6 ± 13.5 | -1.9 ± 4.3 | 13.5 ± 20.6 | -8.9 ± 21.4 | -4.5 ± 7.5 |
NORTH-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0 | 0 ± 0.3 | 1.5 ± 7.1 | 0.1 ± 3.5 | 1.6 ± 8 | 0.6 ± 13.3 | -2.3 ± 16 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 0.3 | 0 ± 6.2 | 0 ± 3.4 | 0 ± 7.1 | 0 ± 13.9 | 0 ± 16.5 |
| 3 | historical | 1985_2015 | 0 ± 0 | -0.1 ± 0.3 | 0.3 ± 5.8 | -0.1 ± 3.3 | 0.1 ± 6.7 | -0.9 ± 14.2 | 0.7 ± 16.4 |
| 4 | SSP245 | 2030_2060 | 0 ± 0 | 0 ± 0.4 | 2.2 ± 7 | 0.5 ± 3.5 | 2.7 ± 7.9 | 3 ± 13.1 | -5.7 ± 16.1 |
| 7 | SSP245 | 2070_2100 | 0 ± 0 | 0.1 ± 0.4 | 2.1 ± 6.7 | 0.9 ± 3.8 | 3.1 ± 7.7 | 5.2 ± 13.3 | -8.4 ± 16.1 |
| 5 | SSP370 | 2030_2060 | 0 ± 0 | 0.1 ± 0.4 | 1.6 ± 6.5 | 0.4 ± 3.6 | 2.1 ± 7.5 | 2.5 ± 13.8 | -4.7 ± 16.9 |
| 8 | SSP370 | 2070_2100 | 0.1 ± 0 | 0.4 ± 0.7 | 3.3 ± 5.9 | 1 ± 3.1 | 4.8 ± 6.8 | 3.9 ± 14.4 | -8.8 ± 16.2 |
| 6 | SSP585 | 2030_2060 | 0 ± 0 | 0.1 ± 0.4 | 2.3 ± 6.8 | 1 ± 3.7 | 3.4 ± 7.8 | 3.7 ± 13.4 | -7.2 ± 16.5 |
| 9 | SSP585 | 2070_2100 | 0.2 ± 0 | 0.6 ± 1.4 | 2.7 ± 7.5 | 1 ± 3.5 | 4.5 ± 8.4 | 7.6 ± 13.5 | -12 ± 16.3 |
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0.1 ± 0 | 0.7 ± 37.1 | -0.1 ± 24.4 | -0.2 ± 4.4 | 0.5 ± 44.6 | -0.4 ± 17.9 | -0.2 ± 0.4 |
| 2 | historical | 1970_2000 | 0 ± 0 | 0 ± 37.3 | 0 ± 24.6 | 0 ± 4.4 | 0 ± 44.9 | 0 ± 18.1 | 0 ± 0.6 |
| 3 | historical | 1985_2015 | 0 ± 0 | 2.1 ± 37.1 | -1.3 ± 25 | 0 ± 4.5 | 0.8 ± 45 | -0.9 ± 17.8 | 0.1 ± 0.5 |
| 4 | SSP245 | 2030_2060 | 5.2 ± 0 | 4.5 ± 35.3 | -5.1 ± 23 | -1.1 ± 4 | 3.5 ± 42.4 | -3.4 ± 17.5 | -0.2 ± 0.5 |
| 7 | SSP245 | 2070_2100 | 5.3 ± 0 | 6.6 ± 34.5 | -7.7 ± 21.8 | -0.6 ± 4.5 | 3.6 ± 41 | -3.1 ± 17.5 | -0.4 ± 0.4 |
| 5 | SSP370 | 2030_2060 | 5.5 ± 0 | 3.5 ± 34.8 | -3.4 ± 23.2 | -1 ± 4 | 4.6 ± 42 | -4.4 ± 15.1 | -0.2 ± 0.4 |
| 8 | SSP370 | 2070_2100 | 5.3 ± 10.2 | 6.8 ± 32.9 | -8.3 ± 22.2 | -1.1 ± 4.5 | 2.7 ± 41.3 | -2.5 ± 20.7 | -0.2 ± 0.5 |
| 6 | SSP585 | 2030_2060 | 0.4 ± 3.6 | 4.3 ± 36.6 | -3 ± 24.1 | -0.1 ± 4.8 | 1.6 ± 44.2 | -1.6 ± 18.8 | -0.2 ± 0.5 |
| 9 | SSP585 | 2070_2100 | 4.1 ± 6.4 | 8.1 ± 34.2 | -7.4 ± 21.7 | -1.3 ± 4 | 3.5 ± 41.2 | -3.1 ± 17.9 | -0.5 ± 0.4 |
SOUTH-AMERICA
| model | period | Hyperarid | Arid | Semi-Arid | Dry subhumid | Sum drylands | Humid | Cold | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | historical | 1850_1880 | 0 ± 0.1 | 0.3 ± 2.1 | 1.3 ± 9.7 | 0.1 ± 3.5 | 1.7 ± 10.5 | -1.3 ± 13.8 | -0.4 ± 2.8 |
| 2 | historical | 1970_2000 | 0 ± 0.1 | 0 ± 1.8 | 0 ± 6.1 | 0 ± 3.3 | 0 ± 7.1 | 0 ± 10.3 | 0 ± 3.7 |
| 3 | historical | 1985_2015 | 0.1 ± 0.1 | 0.2 ± 1.8 | 0.6 ± 6.7 | 0.2 ± 3.3 | 1.1 ± 7.7 | -0.9 ± 10.6 | -0.2 ± 3.7 |
| 4 | SSP245 | 2030_2060 | 0.1 ± 0.2 | 0.9 ± 2.6 | 3.5 ± 10.5 | 1.5 ± 4.3 | 6 ± 11.6 | -5.1 ± 15 | -1 ± 2.8 |
| 7 | SSP245 | 2070_2100 | 0.1 ± 0.1 | 1.5 ± 3.7 | 4.4 ± 10.1 | 2 ± 5.1 | 8 ± 11.9 | -7 ± 16.4 | -1 ± 2.7 |
| 5 | SSP370 | 2030_2060 | 0.1 ± 0.1 | 0.4 ± 2.1 | 2.4 ± 7 | 1.5 ± 4.4 | 4.4 ± 8.5 | -4 ± 11.5 | -0.4 ± 3.1 |
| 8 | SSP370 | 2070_2100 | 0.1 ± 0.1 | 1.5 ± 2.8 | 4.7 ± 8.2 | 1.4 ± 4.4 | 7.7 ± 9.8 | -7.2 ± 13 | -0.5 ± 3.2 |
| 6 | SSP585 | 2030_2060 | 0.1 ± 0.1 | 1.3 ± 2.8 | 4.7 ± 10 | 1.9 ± 5 | 8 ± 11.5 | -6.9 ± 15 | -1.1 ± 2.7 |
| 9 | SSP585 | 2070_2100 | 0.5 ± 1 | 2.6 ± 4 | 5.7 ± 10.4 | 2.4 ± 4.5 | 11.2 ± 12.1 | -10.1 ± 14.6 | -1.2 ± 2.7 |
9.3.3 Changes in proportion of aridity categories, by continent, calculated by model
cmip6$cat.AI <- factor(cmip6$cat.AI, levels = c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid", "Cold"))
tab.percent.cont <- cmip6 %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(period, model, Continent, cat.AI, source) %>%
summarise(count = n()) %>%
ungroup() %>%
group_by(period, model, Continent, cat.AI) %>%
summarise(mmmean = mean(count, na.rm = T), mmsd = sd(count, na.rm = T)) %>%
mutate(percent = round(mmmean/sum(mmmean)*100, 1), sd.percent = round(mmsd/sum(mmmean)*100,1))
write.table(tab.percent.cont, "tab.percent.continent.txt")
tab.cont <- cmip6 %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(period, model, Continent, cat.AI, source) %>%
summarise(count = n()) %>%
ungroup()
list_ref <- list()
for(i in unique(tab.cont$source)){
tab.cont.i <- filter(tab.cont, source == i)
tab.cont.i.ref <- filter(tab.cont.i, period == "1970_2000")
for(j in unique(tab.cont.i$Continent)){
tab.cont.i.j <- filter(tab.cont.i, Continent == j)
tab.cont.i.j.ref <- filter(tab.cont.i.ref, Continent == j)
for(k in unique(tab.cont.i$cat.AI)){
index <- paste(i, j, k, sep = "_")
tab.cont.i.j.k <- filter(tab.cont.i.j, cat.AI == k)
lines <- ifelse(dim(tab.cont.i.j.k)[1] > 0, dim(tab.cont.i.j.k)[1] - 1, 1)
tab.cont.i.j.k.ref <- subset(tab.cont.i.j.ref, cat.AI == k)
if(dim(tab.cont.i.j.k.ref)[1] == 0){
names1 <- names(tab.cont.i.j.k.ref)
line1 <- c("1970_2000","historical", i, j, k, 0)
tab.cont.i.j.k.ref <- rbind(tab.cont.i.j.k.ref, line1) %>%
setNames(names1)
}else{
tab.cont.i.j.k.ref <- tab.cont.i.j.k.ref
}
tab.cont.i.j.k.ref <- tab.cont.i.j.k.ref %>% rbind(tab.cont.i.j.k.ref[rep(1,lines),])
tab.cont.i.j.k$diff <- tab.cont.i.j.k$count - as.numeric(tab.cont.i.j.k.ref$count)
list_ref[[index]] <- tab.cont.i.j.k
}
}
}
tab.cont.diff <- bind_rows(list_ref, .id = "column_label") %>%
group_by(period, model, Continent, cat.AI) %>%
summarise(diff.mean = mean(diff, na.rm = T), diff.sd = sd(diff, na.rm = T))
write.table(tab.cont.diff, "tab.cont.diff.txt")
tab.cont.diff <- read.table("tab.cont.diff.txt")
tab_list_percent <- list()
tab_list_sd <- list()
for(i in unique(tab.cont.diff$Continent)){
tab.i <- subset(tab.cont.diff, Continent == i)
df.percent <- tab.i %>% reshape2::dcast(period + model ~ cat.AI, value.var = "diff.mean")
df.sd <- tab.i %>% reshape2::dcast(period + model ~ cat.AI, value.var = "diff.sd")
# sd for a sum: square root of sum of squared sd
drycats <- which(names(df.percent) %in% c("Hyperarid","Arid","Semi-arid","Dry subhumid"))
df.percent <- df.percent %>% mutate("Sum drylands" = rowSums(.[drycats], na.rm = T))
df.sd <- df.sd %>% mutate("Sum drylands" = sqrt(rowSums((.[drycats])^2, na.rm = T)),2)
missing <- setdiff(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"), names(df.percent))
df.percent[, missing] <- 0
df.sd[,missing] <- 0
df.percent <- df.percent %>% select(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
df.percent[is.na(df.percent)] <- 0
df.percent[,c(3:9)] <- round(df.percent[,c(3:9)], 1)
tab_list_percent[[i]] <- df.percent
df.sd <- df.sd %>% select(c("model","period","Hyperarid","Arid","Semi-arid","Dry subhumid","Sum drylands","Humid","Cold","NA"))
df.sd[is.na(df.sd)] <- 0
df.sd[ , c(3:9)] <- round(df.sd[, c(3:9)], 1)
tab_list_sd[[i]] <- df.sd
}
k_list <- list()
for(i in names(tab_list_percent)){
df <- tab_list_percent[[i]] %>% select(c("model", "period"))
df$Hyperarid <- paste(tab_list_percent[[i]]$Hyperarid, tab_list_sd[[i]]$Hyperarid, sep = " ± ")
df$Arid <- paste(tab_list_percent[[i]]$Arid, tab_list_sd[[i]]$Arid, sep = " ± ")
df$'Semi-Arid' <- paste(tab_list_percent[[i]][,5], tab_list_sd[[i]][,5], sep = " ± ")
df$'Dry subhumid' <- paste(tab_list_percent[[i]][,6], tab_list_sd[[i]][,6], sep = " ± ")
df$'Sum drylands' <- paste(tab_list_percent[[i]][,7], tab_list_sd[[i]][,7], sep = " ± ")
df$Humid <- paste(tab_list_percent[[i]]$Humid, tab_list_sd[[i]]$Humid, sep = " ± ")
df$Cold <- paste(tab_list_percent[[i]]$Cold, tab_list_sd[[i]]$Cold, sep = " ± ")
k_list[[i]] <- kable(df[order(df$model, decreasing = F)], caption = i, digits = 2) %>% kable_styling(bootstrap_options = "bordered") %>%
column_spec(8, italic = T, background = "#DAE2F7") %>% row_spec(2, bold = T)
}
k_list| model | period | Hyperarid | Arid | Sum drylands | Semi-Arid | Humid | Dry subhumid | Cold |
|---|---|---|---|---|---|---|---|---|
| historical | 1850_1880 | -5.8 ± 24.1 | 3 ± 22.7 | 1.5 ± 48.9 | 4.8 ± 34 | 2.8 ± 38.7 | -0.4 ± 12 | 0 ± 0 |
| historical | 1970_2000 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 |
| historical | 1985_2015 | -10.5 ± 19.5 | 11 ± 21.6 | 2.8 ± 33.1 | 5.9 ± 11.9 | -2.7 ± 15.5 | -3.5 ± 10.1 | -2 ± 0 |
| SSP245 | 2030_2060 | -10.5 ± 41 | 24.3 ± 29.7 | 42.2 ± 66.6 | 25.8 ± 40.3 | -37.8 ± 53.2 | 2.5 ± 15.5 | 0 ± 0 |
| SSP370 | 2030_2060 | -29.5 ± 88.4 | -5.2 ± 60.9 | 23.7 ± 145 | 45.5 ± 86.1 | -21.4 ± 43.4 | 12.9 ± 45.6 | -30 ± 0 |
| SSP585 | 2030_2060 | -7.8 ± 50.9 | 13.6 ± 39.6 | 39 ± 82.2 | 33.2 ± 47.6 | -34.6 ± 52.7 | 0 ± 18.4 | 0 ± 0 |
| SSP245 | 2070_2100 | 10.1 ± 66.8 | 9 ± 46.2 | 58.8 ± 99 | 42.1 ± 51.9 | -54.5 ± 61.7 | -2.3 ± 22.7 | 0 ± 0 |
| SSP370 | 2070_2100 | 19.5 ± 66.3 | 18.5 ± 58.2 | 69.2 ± 103.5 | 31.1 ± 46.6 | -67.1 ± 52.3 | 0.2 ± 27.4 | -28 ± 0 |
| SSP585 | 2070_2100 | 45.6 ± 209.6 | 9.2 ± 79.8 | 67.5 ± 240.3 | 16.5 ± 83.8 | -62.8 ± 98 | -3.8 ± 20.8 | 0 ± 0 |
| model | period | Hyperarid | Arid | Sum drylands | Semi-Arid | Humid | Dry subhumid | Cold |
|---|---|---|---|---|---|---|---|---|
| historical | 1850_1880 | 4.3 ± 28 | 8.8 ± 41.5 | 40.2 ± 88.6 | 11.3 ± 61.5 | 90.2 ± 176.8 | 15.8 ± 39.6 | -130.5 ± 287.7 |
| historical | 1970_2000 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 |
| historical | 1985_2015 | -5.2 ± 20 | -12.6 ± 42 | -36.4 ± 57.8 | -17 ± 29.8 | -33.6 ± 169.1 | -1.6 ± 17.2 | 70 ± 234.1 |
| SSP245 | 2030_2060 | -15.7 ± 38.1 | 24.2 ± 40.5 | 57.8 ± 123.7 | 22.2 ± 88.9 | 192.7 ± 202.9 | 27 ± 65.5 | -250.5 ± 375.8 |
| SSP370 | 2030_2060 | -23.8 ± 61.4 | 21.6 ± 45.7 | -4.5 ± 98.3 | 0 ± 55.9 | 174.2 ± 120.1 | -2.3 ± 26 | -169.7 ± 100.4 |
| SSP585 | 2030_2060 | -1.8 ± 30.8 | 23.8 ± 34.1 | 72.9 ± 115.9 | 27.1 ± 91.2 | 250 ± 201.3 | 23.8 ± 54.9 | -322.9 ± 358.7 |
| SSP245 | 2070_2100 | -8.5 ± 31.9 | 23.6 ± 37.9 | 68.1 ± 133.8 | 30.1 ± 101.2 | 294.5 ± 208.7 | 22.8 ± 72.2 | -362.5 ± 381.8 |
| SSP370 | 2070_2100 | 40.1 ± 64.3 | 25.3 ± 63.8 | 101.2 ± 108.9 | 32.7 ± 53 | 241.2 ± 179.9 | 3.1 ± 29 | -342.3 ± 156 |
| SSP585 | 2070_2100 | 55.1 ± 178.4 | 59.5 ± 146 | 215 ± 313.2 | 77.9 ± 202.8 | 324.1 ± 262.6 | 22.5 ± 61.9 | -539.1 ± 416.4 |
CENTRAL-AMERICA
| model | period | Hyperarid | Arid | Sum drylands | Semi-Arid | Humid | Dry subhumid | Cold |
|---|---|---|---|---|---|---|---|---|
| historical | 1850_1880 | 0 ± 0 | -0.3 ± 4.7 | 2.5 ± 21.5 | -0.5 ± 18 | -2 ± 28.2 | 3.3 ± 10.8 | 0 ± 0 |
| historical | 1970_2000 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 |
| historical | 1985_2015 | 0 ± 0 | 1.7 ± 4 | 4.2 ± 8.6 | 2.5 ± 6.3 | -4 ± 5 | -0.1 ± 4.3 | -2 ± 0 |
| SSP245 | 2030_2060 | 0 ± 0 | 9.8 ± 11.1 | 24 ± 29.7 | 17.3 ± 25.7 | -23.5 ± 26.8 | -3.2 ± 10 | 0 ± 0 |
| SSP370 | 2030_2060 | 0 ± 0 | 11.8 ± 12.4 | 15.1 ± 31.2 | 3.3 ± 24 | -14.6 ± 37.3 | 0 ± 15.6 | 0 ± 0 |
| SSP585 | 2030_2060 | 0 ± 0 | 12.8 ± 14 | 31.5 ± 36.2 | 21.1 ± 29.1 | -31 ± 25.5 | -2.5 ± 16.4 | 0 ± 0 |
| SSP245 | 2070_2100 | 0 ± 0 | 11 ± 10.3 | 31.9 ± 36.4 | 22.6 ± 29.2 | -31.5 ± 27.3 | -1.7 ± 19 | 0 ± 0 |
| SSP370 | 2070_2100 | 12 ± 15.6 | 23.2 ± 15.1 | 50.2 ± 39 | 18.7 ± 29.5 | -39.6 ± 23.8 | -3.7 ± 13.4 | 0 ± 0 |
| SSP585 | 2070_2100 | 6 ± 7.1 | 25.2 ± 24.9 | 60.6 ± 42.8 | 35.6 ± 28.6 | -55.1 ± 42.4 | -6.2 ± 18.6 | 0 ± 0 |
| model | period | Hyperarid | Arid | Sum drylands | Semi-Arid | Humid | Dry subhumid | Cold |
|---|---|---|---|---|---|---|---|---|
| historical | 1850_1880 | 0 ± 0 | 4.7 ± 11.2 | 20.7 ± 20.7 | 8.2 ± 13.3 | 50.3 ± 106.6 | 7.8 ± 11.3 | -70 ± 115 |
| historical | 1970_2000 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 |
| historical | 1985_2015 | 0 ± 0 | -2.2 ± 9.8 | -9.2 ± 27.8 | -5.8 ± 24.6 | 12.9 ± 48.5 | -1.2 ± 8.5 | -4.3 ± 73.1 |
| SSP245 | 2030_2060 | 0 ± 0 | 4.7 ± 7.4 | 33.7 ± 18.9 | 17.5 ± 14.6 | 97.5 ± 126 | 11.5 ± 9.6 | -128.5 ± 146.3 |
| SSP370 | 2030_2060 | 0 ± 0 | 7.2 ± 8.5 | 35.2 ± 16.1 | 17.6 ± 11.2 | 90.5 ± 75.1 | 10.4 ± 7.8 | -125.2 ± 73.5 |
| SSP585 | 2030_2060 | 0 ± 0 | 9.8 ± 8.2 | 41.1 ± 18.4 | 19.1 ± 14.2 | 125.9 ± 134.4 | 12.2 ± 8.4 | -164.8 ± 144.4 |
| SSP245 | 2070_2100 | 0 ± 0 | 8.6 ± 11.2 | 44.2 ± 24.7 | 21.6 ± 19.9 | 123.1 ± 120.8 | 14 ± 9.3 | -166.6 ± 139.5 |
| SSP370 | 2070_2100 | 0 ± 0 | 19.2 ± 13.7 | 86.9 ± 44.6 | 37.7 ± 28 | 92.2 ± 110 | 30 ± 31.8 | -177.7 ± 82.2 |
| SSP585 | 2070_2100 | 0 ± 0 | 18.3 ± 19 | 81.8 ± 39.7 | 37.4 ± 24.7 | 150.7 ± 131 | 26.1 ± 24.6 | -230.9 ± 148.4 |
EUROPE-AFRICA
| model | period | Hyperarid | Arid | Sum drylands | Semi-Arid | Humid | Dry subhumid | Cold |
|---|---|---|---|---|---|---|---|---|
| historical | 1850_1880 | -2.1 ± 5.2 | -1.2 ± 9 | -1.8 ± 19.9 | 1.8 ± 14.9 | 1 ± 20.7 | -0.4 ± 8.2 | 13 ± 0 |
| historical | 1970_2000 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 |
| historical | 1985_2015 | 0.5 ± 5.8 | 0.2 ± 8.2 | -1.4 ± 24.4 | -4.2 ± 17.9 | 2.5 ± 16.7 | 2 ± 13.2 | -6.5 ± 9.2 |
| SSP245 | 2030_2060 | 11.2 ± 8.1 | 7 ± 10 | 26.5 ± 25.8 | 11.6 ± 19.8 | -22.2 ± 35.5 | -3.3 ± 10.1 | -54 ± 0 |
| SSP370 | 2030_2060 | 14 ± 6.8 | 6.1 ± 9.9 | 26.1 ± 20.3 | 10.5 ± 14.3 | -23.1 ± 20.7 | -4.5 ± 8 | -56 ± 0 |
| SSP585 | 2030_2060 | 16.9 ± 9.9 | 7.8 ± 10.2 | 35.5 ± 25.8 | 16.5 ± 17.2 | -33.2 ± 32 | -5.8 ± 13 | -57 ± 0 |
| SSP245 | 2070_2100 | 19.4 ± 8.2 | 6.8 ± 11.4 | 38.6 ± 29 | 14.9 ± 23.1 | -36.5 ± 35.9 | -2.5 ± 10.7 | 0 ± 0 |
| SSP370 | 2070_2100 | 25.3 ± 16.7 | -0.9 ± 23.1 | 35.5 ± 41.9 | 17.7 ± 22.1 | -30.9 ± 28.5 | -6.5 ± 21.3 | 0 ± 0 |
| SSP585 | 2070_2100 | 32.4 ± 12.3 | 11.3 ± 19.9 | 56.3 ± 36.9 | 21.2 ± 22.1 | -55.5 ± 27.4 | -8.7 ± 18.1 | 0 ± 0 |
NORTH-AMERICA
| model | period | Hyperarid | Arid | Sum drylands | Semi-Arid | Humid | Dry subhumid | Cold |
|---|---|---|---|---|---|---|---|---|
| historical | 1850_1880 | 0 ± 0 | 0.4 ± 3.1 | 37 ± 71.9 | 31.8 ± 66.3 | 21.7 ± 91.3 | 4.9 ± 27.7 | -56.1 ± 174 |
| historical | 1970_2000 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 |
| historical | 1985_2015 | 0 ± 0 | 0.3 ± 4.2 | -5.4 ± 48.8 | -3 ± 32.2 | -19.8 ± 70 | -2.7 ± 36.4 | 25.1 ± 114 |
| SSP245 | 2030_2060 | 0 ± 0 | 3.4 ± 2.9 | 77.5 ± 109.8 | 60 ± 104.2 | 82.2 ± 94.5 | 14.2 ± 34.7 | -159.2 ± 214.3 |
| SSP370 | 2030_2060 | 0 ± 0 | 5.5 ± 6.9 | 62 ± 56.5 | 44.5 ± 45.2 | 70.5 ± 48.5 | 12.1 ± 33.2 | -131.6 ± 77 |
| SSP585 | 2030_2060 | 0 ± 0 | 7.1 ± 7.9 | 99 ± 102.3 | 62.1 ± 91 | 103.4 ± 97.9 | 29.8 ± 46.2 | -201.3 ± 203.2 |
| SSP245 | 2070_2100 | 0 ± 0 | 4.7 ± 3.1 | 87.1 ± 99.3 | 56.9 ± 86 | 145.9 ± 122.4 | 25.5 ± 49.7 | -231.9 ± 215.6 |
| SSP370 | 2070_2100 | 4 ± 0 | 15.7 ± 20 | 139.6 ± 80.2 | 91.5 ± 59.6 | 108 ± 110.3 | 28.4 ± 49.8 | -243.9 ± 106 |
| SSP585 | 2070_2100 | 5 ± 0 | 20.2 ± 38.4 | 147.1 ± 145.7 | 91.2 ± 136 | 198.8 ± 99.8 | 30.8 ± 35.4 | -339.6 ± 220.2 |
| model | period | Hyperarid | Arid | Sum drylands | Semi-Arid | Humid | Dry subhumid | Cold |
|---|---|---|---|---|---|---|---|---|
| historical | 1850_1880 | 1 ± 0 | 6 ± 42.8 | 4.5 ± 57.7 | -1.2 ± 38.1 | -3 ± 12.1 | -1.2 ± 6.8 | 0.2 ± 1.2 |
| historical | 1970_2000 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 |
| historical | 1985_2015 | 1 ± 0 | 5.1 ± 32.9 | 5.6 ± 40.3 | -2.2 ± 21.3 | -3 ± 7.9 | 1.8 ± 9.4 | -0.2 ± 1 |
| SSP245 | 2030_2060 | 45 ± 0 | 50.2 ± 62.1 | 62.8 ± 74.9 | -26.5 ± 39.9 | -19.8 ± 18.3 | -5.8 ± 12.4 | -1.5 ± 0.7 |
| SSP370 | 2030_2060 | 47 ± 0 | 42.4 ± 50.3 | 71.7 ± 61.2 | -12.4 ± 33.3 | -27 ± 61.2 | -5.3 ± 10.3 | -3.3 ± 3.2 |
| SSP585 | 2030_2060 | 23.5 ± 0.7 | 33.8 ± 55.9 | 33.3 ± 71.6 | -23.2 ± 43.1 | -12 ± 20.1 | -0.8 ± 12.2 | -1.5 ± 0.7 |
| SSP245 | 2070_2100 | 45 ± 0 | 66.6 ± 81.5 | 60.7 ± 101.2 | -48.5 ± 58.5 | -17.4 ± 18.8 | -2.5 ± 13 | -5 ± 0 |
| SSP370 | 2070_2100 | 70.7 ± 84 | 63.2 ± 110.8 | 70 ± 153.2 | -57 ± 62.9 | -14.2 ± 57.5 | -6.8 ± 13.5 | -5 ± 0 |
| SSP585 | 2070_2100 | 60.3 ± 48.7 | 69.8 ± 126.8 | 71.2 ± 164.5 | -50 ± 91.8 | -23.9 ± 22.1 | -9 ± 14.2 | 0 ± 0 |
SOUTH-AMERICA
| model | period | Hyperarid | Arid | Sum drylands | Semi-Arid | Humid | Dry subhumid | Cold |
|---|---|---|---|---|---|---|---|---|
| historical | 1850_1880 | -0.1 ± 0.4 | 5.1 ± 13.9 | 26.3 ± 86.6 | 20.1 ± 83.4 | -20.6 ± 79.5 | 1.3 ± 18.8 | -6.8 ± 35.1 |
| historical | 1970_2000 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 ± 0 |
| historical | 1985_2015 | 0.5 ± 1.2 | 2.7 ± 5.9 | 16.7 ± 19.2 | 10.5 ± 15.7 | -14.2 ± 13.2 | 3 ± 9.3 | -2.5 ± 5.8 |
| SSP245 | 2030_2060 | 1.6 ± 1.1 | 13.2 ± 26.6 | 93.8 ± 100 | 55.6 ± 93.1 | -78.5 ± 97.2 | 23.5 ± 24.8 | -18.8 ± 42 |
| SSP370 | 2030_2060 | 1.2 ± 0.9 | 5.9 ± 19.7 | 69.4 ± 52.4 | 38.2 ± 31.9 | -59.5 ± 55.6 | 24.1 ± 36.6 | -12.9 ± 18.7 |
| SSP585 | 2030_2060 | 1.5 ± 1.1 | 19.2 ± 29.9 | 123.7 ± 97.4 | 72.8 ± 84.5 | -107.5 ± 99.4 | 30.2 ± 38.1 | -20.1 ± 41.2 |
| SSP245 | 2070_2100 | 1.4 ± 1 | 22.6 ± 49.9 | 124 ± 111.5 | 68.9 ± 90.8 | -107 ± 130.1 | 31 ± 41.3 | -22.8 ± 43.7 |
| SSP370 | 2070_2100 | 2 ± 2.3 | 22.9 ± 23.8 | 121.2 ± 92.7 | 73.5 ± 82.1 | -109.5 ± 109.3 | 22.7 ± 35.9 | -15 ± 15.1 |
| SSP585 | 2070_2100 | 7.4 ± 15.3 | 40.5 ± 51.6 | 182.8 ± 140.1 | 96.9 ± 122.8 | -160.2 ± 133.1 | 38 ± 40.7 | -29.4 ± 47.8 |
10 Changes in proportion of aridity categories, figure
10.1 Colplot by continent
tab.flow <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 ) %>%
group_by(period, model, Continent, cat.AI) %>%
summarise(count = n()) %>%
ungroup() %>% group_by(period, model, Continent) %>% mutate(percent = round(count/sum(count)*100, 1), count = NULL)
tab.flow$Continent <- str_replace(tab.flow$Continent, "-", "\n")
df245 <- rbind(subset(tab.flow, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.flow, model == "SSP245")) %>% mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.AI %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
b245 <- ggplot(data = df245, aes(x = period, y = percent))+
geom_col(aes(group = cat.AI, col = cat.AI, fill = cat.AI))+
geom_label(aes(y = lab.y, label = percent))+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
facet_grid(rows = vars(Continent), switch = "y")+
scale_y_continuous(position = "right")+
labs(title = "SSP 2-4.5", y = "%", x = "")+
theme_minimal()
df370 <- rbind(subset(tab.flow, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.flow, model == "SSP370")) %>% mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.AI %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
b370 <- ggplot(data = df370, aes(x = period, y = percent))+
geom_col(aes(group = cat.AI, col = cat.AI, fill = cat.AI))+
geom_label(aes(y = lab.y, label = percent))+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
facet_grid(rows = vars(Continent), switch = "y")+
scale_y_continuous(position = "right")+ labs(title = "SSP 3-7.0", y = "%", x = "")+
theme_minimal()
df585 <- rbind(subset(tab.flow, model == "historical" & period %in% c("1850_1880", "1970_2000")), subset(tab.flow, model == "SSP585")) %>% mutate(lab.y = (rev(cumsum(rev(percent)))) - percent*0.5) %>%
subset(cat.AI %in% c("Hyperarid", "Arid", "Semi-arid", "Dry subhumid", "Humid","Cold"))
b585 <- ggplot(data = df585, aes(x = period, y = percent))+
geom_col(aes(group = cat.AI, col = cat.AI, fill = cat.AI))+
geom_label(aes(y = lab.y, label = percent))+
scale_color_manual(values = col.cat, aesthetics = c("col", "fill"), na.translate = F)+
facet_grid(rows = vars(Continent), switch = "y")+
scale_y_continuous(position = "right")+ labs(title = "SSP 5-8.5", y = "%", x = "")+
theme_minimal()
ggarrange(plotlist = list(b245, b370, b585), common.legend = T, legend = "bottom", ncol = 3)
11 Evolution of aridity index by Continent and period
11.1 Map with labels
tab.percent.region <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 & period != "1985_2015") %>%
group_by(period, model, Continent, Name) %>% select(c("diff.AI.mean.percent")) %>%
summarise(AI.diff.mean = mean(diff.AI.mean.percent, na.rm = T), AI.diff.sd= sd(diff.AI.mean.percent, na.rm = T)) %>%
ungroup()
ipcc_regions.sf <- st_as_sf(ipcc_regions)
percent.region.sf <- merge(ipcc_regions.sf, tab.percent.region, all.y = T)pbreaks <- c(-10, 0, 10, 20, 30, 40)
colscale <- c("#08519c", "#3182bd", "#6baed6", "#9ecae1", "azure","cornsilk", "#f4a582")
#colscale <- c("#c6dbef","white","#fddbc7", "#f4a582", "#d6604d", "#b2182b", "#993344")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot(data = subset(percent.region.sf, period == j & model == i)) + geom_sf(aes(fill = AI.diff.mean))+
binned_scale(aesthetics = "fill", breaks = pbreaks, palette = function(x) rev(colscale))+
borders(colour = "grey60")+
geom_sf_label(aes(label = round(AI.diff.mean,1)),size = 2) +
labs(title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
11.2 Point figure
11.2.0.1 By Continent
cmip6s$diff.AI.mean.percent <- with(cmip6s, (AI.mean-AI.ref.mean)/AI.ref.mean*100)
tab.percent.continent <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 & period != "1985_2015") %>%
group_by(period, model, Continent) %>% select(c("diff.AI.mean.percent")) %>%
summarise(AI.diff.mean = mean(diff.AI.mean.percent, na.rm = T), AI.diff.sd= sd(diff.AI.mean.percent, na.rm = T), AI.diff.cv = AI.diff.sd/abs(AI.diff.mean)) %>%
ungroup()
df <- tab.percent.continent %>% filter(period != "1850_1880" & Continent != "POLAR")
ref <- df[which(df$model == "historical"),]
ref1 <- ref %>% mutate(model = "SSP245")
ref2 <- ref %>% mutate(model = "SSP370")
ref3 <- ref %>% mutate(model = "SSP585")
dfref <- rbind(subset(df, model != "historical"), ref1) %>% rbind(ref2) %>% rbind(ref3)
dfref$Continent <- str_replace(dfref$Continent, "-", "\n")
dfref$Continent <- factor(dfref$Continent, levels = c("NORTH\nAMERICA", "CENTRAL\nAMERICA", "SOUTH\nAMERICA", "EUROPE", "MED\nBASIN", "AFRICA", "ASIA", "OCEANIA"))
dfref$model[which(dfref$model == "SSP245")] <- "SSP 2-4.5"
dfref$model[which(dfref$model == "SSP370")] <- "SSP 3-7.0"
dfref$model[which(dfref$model == "SSP585")] <- "SSP 5-8.5"
dfref$period[which(dfref$period == "1970_2000")] <- "1970-2000"
dfref$period[which(dfref$period == "2030_2060")] <- "2030-2060"
dfref$period[which(dfref$period == "2070_2100")] <- "2070-2100"
ggplot(dfref, aes(x = period, y = AI.diff.mean))+
geom_point(aes(size = AI.diff.cv, col = AI.diff.cv))+
geom_line(aes(group = Continent), size = 0.1)+
facet_grid(rows = vars(model), cols = vars(Continent))+
scale_size_continuous(breaks = c(0,1,5,10,100), transform = "pseudo_log")+
scale_color_gradient(low = "gray", high = "#D73027", breaks = c(0,1,5,10,100), transform = "pseudo_log")+
labs(y = "Difference in aridity index compared to 1970-2000, %", x = "")+
guides(color = guide_legend("Coefficient of variance"), size = guide_legend("Coefficient of variance"))+
ylim(-40,25)+
theme_minimal()+
theme(legend.position = "bottom", axis.text.x = element_text(angle = 90))
knitr::kable(dfref) %>% kable_styling(bootstrap_options = "bordered")| period | model | Continent | AI.diff.mean | AI.diff.sd | AI.diff.cv | |||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2030-2060 | SSP 2-4.5 | AFRICA | -1.0179744 | 12.876170 | 12.6488143 | |||||||||||||||||||||||||||||||||
| 2030-2060 | SSP 2-4.5 | ASIA | -3.4101720 | 8.068910 | 2.3661299 | |||||||||||||||||||||||||||||||||
| 2030-2060 | SSP 2-4.5 | CENTRAL AMERICA |
-16.643101
BASIN
|
-18.634281
AMERICA
|
-4.100262
AMERICA
|
-10.531839
AMERICA
|
-13.424730
BASIN
|
-21.449362
AMERICA
|
22.197054
AMERICA
|
-12.712817
AMERICA
|
-20.850032
BASIN
|
-25.043787
AMERICA
|
0.591772
AMERICA
|
-13.182876
AMERICA
|
-21.874522
BASIN
|
-27.588601
AMERICA
|
3.130155
AMERICA
|
-12.760628
AMERICA
|
-26.142999
BASIN
|
-25.843929
AMERICA
|
-13.993980
AMERICA
|
-13.994989
AMERICA
|
-32.095687
BASIN
|
-38.314460
AMERICA
|
-6.192770
AMERICA
|
-19.299671
AMERICA
|
0.000000
BASIN
|
0.000000
AMERICA
|
0.000000
AMERICA
|
0.000000
AMERICA
|
0.000000
BASIN
|
0.000000
AMERICA
|
0.000000
AMERICA
|
0.000000
AMERICA
|
0.000000
BASIN
|
0.000000
AMERICA
|
0.000000
AMERICA
|
0.000000
|
11.2.0.2 By subregion
names(tab.percent.continent) <- c("period","model","Continent","AI.diff.mean.cont","AI.diff.sd.cont","AI.diff.cv.cont")
tab.percent.subregion <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC","POLAR") & lat > -55 & period != "1985_2015") %>%
group_by(period, model, Name, Continent) %>% select(c("diff.AI.mean.percent")) %>%
summarise(AI.diff.mean = mean(diff.AI.mean.percent, na.rm = T), AI.diff.sd= sd(diff.AI.mean.percent, na.rm = T), AI.diff.cv = AI.diff.sd/AI.diff.mean) %>%
ungroup()
tab.percent.subregion <- merge(tab.percent.subregion, tab.percent.continent, by = c("period","model","Continent"), all.x = T)
df <- tab.percent.subregion %>% filter(period != "1850_1880")
ref <- df[which(df$model == "historical"),]
ref1 <- ref %>% mutate(model = "SSP245")
ref2 <- ref %>% mutate(model = "SSP370")
ref3 <- ref %>% mutate(model = "SSP585")
dfref <- rbind(subset(df, model != "historical"), ref1) %>% rbind(ref2) %>% rbind(ref3)
dfref$Name <- str_replace(dfref$Name, "-", "\n")
dfref$model[which(dfref$model == "SSP245")] <- "SSP 2-4.5"
dfref$model[which(dfref$model == "SSP370")] <- "SSP 3-7.0"
dfref$model[which(dfref$model == "SSP585")] <- "SSP 5-8.5"
dfref$period[which(dfref$period == "1970_2000")] <- "1970-2000"
dfref$period[which(dfref$period == "2030_2060")] <- "2030-2060"
dfref$period[which(dfref$period == "2070_2100")] <- "2070-2100"
for(i in unique(dfref$Continent)){
df <- filter(dfref, Continent == i)
g <- ggplot(df, aes(x = period))+
geom_point(aes(y = AI.diff.mean, size = AI.diff.cv, col = AI.diff.cv))+
geom_line(aes(y = AI.diff.mean, group = Name), size = 0.1)+
geom_point(aes(y = AI.diff.mean.cont, size = AI.diff.cv.cont), col = "gray", alpha = 0.5)+
geom_line(aes(y = AI.diff.mean.cont, group = Name), size = 0.1, alpha = 0.5)+
facet_grid(cols = vars(model), rows = vars(Name), switch = "both")+
scale_size_continuous(breaks = c(0,1,5,10,100), transform = "pseudo_log")+
scale_color_gradient(low = "#ABD9E9", high = "#D73027", breaks = c(0,1,5,10,100), transform = "pseudo_log")+
labs(y = "Difference in aridity index compared to 1970-2000, %", x = "", title = i)+
guides(color = guide_legend("Standard deviation"), size = guide_legend("Standard deviation"))+
theme_minimal()+
theme(legend.position = "bottom", axis.text.x = element_text(angle = 90))+
scale_y_continuous(position = "right", limits = c(-40,30))
print(g)
}
11.3 Change AI in boxplot
tab.percent.continent <- cmip6s %>%
subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 & period %in% c("1970_2000","2030_2060","2070_2100"))
df <- tab.percent.continent %>% filter(Continent != "POLAR")
ref <- df[which(df$model == "historical"),]
ref1 <- ref %>% mutate(model = "SSP245")
ref2 <- ref %>% mutate(model = "SSP370")
ref3 <- ref %>% mutate(model = "SSP585")
dfref <- rbind(subset(df, model != "historical"), ref1) %>% rbind(ref2) %>% rbind(ref3)
dfref$Continent <- str_replace(dfref$Continent, "-", "\n")
ggplot(dfref, aes(x = period, y = diff.AI.mean.percent))+
geom_boxplot(aes(col = Continent), outliers = F)+
facet_grid(rows = vars(model), cols = vars(Continent), space = "free_y")+
scale_color_viridis_d(end = 0.8)+
labs(y = "Difference in aridity index compared to 1970-2000, %", x = "")+
guides(color = F)+
theme_minimal()+
theme(legend.position = "bottom", axis.text.x = element_text(angle = 90))
11.4 Point figure precipitation and temperature
cmip6s$pr.percent <- with(cmip6s, diff.pr.mean/spr.ref.mean*100)
tab.percent.continent <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 & period != "1985_2015") %>%
group_by(period, model, Continent) %>% select(c("diff.t.mean", "pr.percent")) %>%
summarise(t.diff.mean = mean(diff.t.mean, na.rm = T), t.diff.sd= sd(diff.t.mean, na.rm = T), t.diff.cv = t.diff.sd/abs(t.diff.mean),
pr.diff.mean = mean(pr.percent, na.rm = T), pr.diff.sd= sd(pr.percent, na.rm = T), pr.diff.cv = pr.diff.sd/abs(pr.diff.mean)) %>%
ungroup()
df <- tab.percent.continent %>% filter(period != "1850_1880" & Continent != "POLAR")
ref <- df[which(df$model == "historical"),]
ref1 <- ref %>% mutate(model = "SSP245")
ref2 <- ref %>% mutate(model = "SSP370")
ref3 <- ref %>% mutate(model = "SSP585")
dfref <- rbind(subset(df, model != "historical"), ref1) %>% rbind(ref2) %>% rbind(ref3)
dfref$Continent <- str_replace(dfref$Continent, "-", "\n")
dfref$Continent <- factor(dfref$Continent, levels = c("NORTH\nAMERICA", "CENTRAL\nAMERICA", "SOUTH\nAMERICA", "EUROPE", "MED\nBASIN", "AFRICA", "ASIA", "OCEANIA"))
dfref$model[which(dfref$model == "SSP245")] <- "SSP 2-4.5"
dfref$model[which(dfref$model == "SSP370")] <- "SSP 3-7.0"
dfref$model[which(dfref$model == "SSP585")] <- "SSP 5-8.5"
dfref$period[which(dfref$period == "1970_2000")] <- "1970-2000"
dfref$period[which(dfref$period == "2030_2060")] <- "2030-2060"
dfref$period[which(dfref$period == "2070_2100")] <- "2070-2100"
ggplot(dfref, aes(x = period))+
geom_point(aes(y = t.diff.mean*3, size = t.diff.cv, col = "Temperature, °C"), alpha = 0.5)+
geom_line(aes(y = t.diff.mean*3, group = Continent, col = "Temperature, °C"), size = 0.1)+
geom_point(aes(y = pr.diff.mean, size = pr.diff.cv, col = "Precipitation, %"), alpha = 0.5)+
geom_line(aes(y = pr.diff.mean, group = Continent, col = "Precipitation, %"), size = 0.1)+
scale_size_continuous(breaks = c(0,1,5,10,20), transform = "pseudo_log")+
scale_color_manual(values = c("#3182bd", "#d6604d"))+
facet_grid(rows = vars(model), cols = vars(Continent))+
labs(y = "Precipitation anomalies, %", x = "", size = "Coefficient of variance", color = "")+
ylim(-45,25)+
theme_minimal()+
theme(legend.position = "bottom", axis.text.x = element_text(angle = 90))+
scale_y_continuous(sec.axis = sec_axis(trans = ~./3, name = "Temperature anomalies, °C"))
knitr::kable(dfref) %>% kable_styling(bootstrap_options = "bordered")| period | model | Continent | t.diff.mean | t.diff.sd | t.diff.cv | pr.diff.mean | pr.diff.sd | pr.diff.cv | ||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2030-2060 | SSP 2-4.5 | AFRICA | 1.631458 | 0.3166157 | 0.1940692 | 7.7904137 | 12.236848 | 1.5707572 | ||||||||||||||||||||||||||||||
| 2030-2060 | SSP 2-4.5 | ASIA | 2.338152 | 0.6685291 | 0.2859220 | 9.6790737 | 6.609489 | 0.6828638 | ||||||||||||||||||||||||||||||
| 2030-2060 | SSP 2-4.5 | CENTRAL AMERICA |
1.68272
BASIN
|
1.98698
AMERICA
|
2.62947
AMERICA
|
1.45728
AMERICA
|
2.03568
BASIN
|
2.42553
AMERICA
|
3.08982
AMERICA
|
1.79399
AMERICA
|
2.29305
BASIN
|
2.66748
AMERICA
|
3.57561
AMERICA
|
2.05472
AMERICA
|
2.47160
BASIN
|
2.89067
AMERICA
|
4.00689
AMERICA
|
2.26903
AMERICA
|
3.47010
BASIN
|
4.02250
AMERICA
|
5.38537
AMERICA
|
3.22043
AMERICA
|
4.15576
BASIN
|
4.84252
AMERICA
|
6.36060
AMERICA
|
3.92026
AMERICA
|
0.00000
BASIN
|
0.00000
AMERICA
|
0.00000
AMERICA
|
0.00000
AMERICA
|
0.00000
BASIN
|
0.00000
AMERICA
|
0.00000
AMERICA
|
0.00000
AMERICA
|
0.00000
BASIN
|
0.00000
AMERICA
|
0.00000
AMERICA
|
0.00000
|
tab.percent.continent <- cmip6s %>% subset(!Continent %in% c("SOUTHERN","PACIFIC","ATLANTIC","INDIAN","ARCTIC") & lat > -55 & period != "1985_2015") %>%
group_by(period, model, Name) %>% select(c("diff.t.mean", "pr.percent")) %>%
summarise(t.diff.mean = mean(diff.t.mean, na.rm = T), t.diff.sd= sd(diff.t.mean, na.rm = T),
pr.diff.mean = mean(pr.percent, na.rm = T), pr.diff.sd= sd(pr.percent, na.rm = T)) %>%
ungroup()
df <- tab.percent.continent %>% filter(period != "1850_1880")
ref <- df[which(df$model == "historical"),]
ref1 <- ref %>% mutate(model = "SSP245")
ref2 <- ref %>% mutate(model = "SSP370")
ref3 <- ref %>% mutate(model = "SSP585")
dfref <- rbind(subset(df, model != "historical"), ref1) %>% rbind(ref2) %>% rbind(ref3)
dfref$Name <- str_replace(dfref$Name, "-", "\n")
ggplot(dfref, aes(x = period))+
geom_point(aes(y = t.diff.mean*3, size = t.diff.sd, col = "Temperature, °C"), alpha = 0.5)+
geom_line(aes(y = t.diff.mean*3, group = Name, col = "Temperature, °C"), size = 0.1)+
geom_point(aes(y = pr.diff.mean, size = pr.diff.sd, col = "Precipitation, %"), alpha = 0.5)+
geom_line(aes(y = pr.diff.mean, group = Name, col = "Precipitation, %"), size = 0.1)+
scale_color_manual(values = c("#3182bd", "#d6604d"))+
facet_grid(cols = vars(model), rows = vars(Name), switch = "both")+
labs(y = "Precipitation anomalies, %", x = "", size = "Standard deviation", color = "")+
ylim(-45,25)+
theme_minimal()+
theme(legend.position = "bottom", axis.text.x = element_text(angle = 90))+
scale_y_continuous(sec.axis = sec_axis(trans = ~./3, name = "Temperature anomalies, °C"))
12 Changes of precipitation and temperature over time and scenarios
12.1 Temperature
12.1.1 Absolute temperature
quantile(cmip6s$t.mean, probs = seq(0,1,0.1))
## 0% 10% 20% 30% 40% 50%
## -53.4587313 -42.5828261 -28.4644864 -17.7502317 -7.2080554 -0.2580962
## 60% 70% 80% 90% 100%
## 7.1198406 15.3318920 22.8072968 26.4971129 34.3727997
t.breaks <- c(-50,-40,-30,-20,-10,0,10,20,30)
colscale <- c("#08519c", "#3182bd", "#6baed6", "#9ecae1", "#c6dbef", "#fddbc7", "#f4a582", "#d6604d", "#b2182b", "#67001f")map_list <- list()
for(i in c("1850_1880", "1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = t.mean))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = t.breaks, palette = function(x) colscale,
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(title = i, fill = "Mean annual temperature, °C")+
theme_void()+ylim(-55,90)+
theme(legend.position = "right")
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 1, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = t.mean))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = t.breaks, palette = function(x) colscale,
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(fill = "Mean annual temperature, °C", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
#labels = c("2030-2060", "2070-2100"12.1.2 Temperature anomalies
quantile(cmip6s$diff.t.mean, probs = seq(0,1,0.1))
## 0% 10% 20% 30% 40% 50% 60%
## -3.5775507 -0.5616741 0.0000000 1.1670755 1.8544034 2.2566573 2.6600679
## 70% 80% 90% 100%
## 3.1444949 3.7454555 4.5875606 10.7627615
t.breaks <- c(-3,-0.5,0,1.5,2,3,4,5,10)
colscale <- c("#08519c", "#3182bd", "#6baed6", "#9ecae1", "#c6dbef", "#fddbc7", "#f4a582", "#d6604d", "#b2182b", "#67001f")map_list <- list()
for(i in c("1850_1880", "1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = diff.t.mean))+
borders(colour = "grey60")+
scale_fill_gradient2(low = "#08519c",mid = "white", high = "#b2182b", midpoint = 0, transform = "pseudo_log", breaks = c(-2, -1, 0, 1, 2, 3, 4))+
labs(fill = "Precipitation anomalies in %", title = i)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom", legend.text = element_text(angle = 0))
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 1, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_tile(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = diff.t.mean))+
borders(colour = "grey60")+
scale_fill_gradient2(low = "#08519c",mid = "white", high = "#b2182b", midpoint = 0, transform = "pseudo_log", breaks = c(-2, -1, 0, 1, 2, 3, 4))+
labs(fill = "Temperature anomalies in °C", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom", legend.text = element_text(angle = 0))
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
### Binned anomalies
YOR <- brewer.pal(n = 11, name ="YlOrRd")
coltemp <- c("[0,1]" = YOR[1], "(1,2]" = YOR[2], "(2,3]" = YOR[3], "(3,4]" = YOR[4], "(4,5]" = YOR[5], "(5,6]" = YOR[6], "(6,7]" = YOR[7], "(7,8]" = YOR[8], "(8,9]" = YOR[9], "(9,10]" = "grey20")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot(data = subset(cmip6s, period == j & model == i),
aes(x=lon, y = lat, fill = cut(diff.t.mean, breaks = c(0,1,2,3,4,5,6,7,8,9,10), include.lowest = T))) +
geom_tile(show.legend = T)+
borders(colour = "grey60")+
scale_fill_manual(values = coltemp, drop = F)+
labs(fill = "Temperature\nanomalies in °C", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom", legend.text = element_text(angle = 0))+
guides(fill = guide_legend(nrow = 2))
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
12.2 Precipitation
12.2.1 Absolute values of precipitations
quantile(cmip6s$pr.mean, probs = seq(0,1,0.1))
## 0% 10% 20% 30% 40% 50%
## 8.579696 55.000319 104.132310 200.852004 337.042710 463.946055
## 60% 70% 80% 90% 100%
## 579.741473 712.099280 959.490392 1431.315602 6863.723388
p.breaks <- c(10,50,100,200,300,400,500,600,700)
colscale <- c("#08519c", "#3182bd", "#6baed6", "#9ecae1", "#c6dbef", "#fddbc7", "#f4a582", "#d6604d", "#b2182b", "#67001f")map_list <- list()
for(i in c("1850_1880","1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = pr.mean))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = p.breaks, palette = function(x) rev(colscale),
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(title = i, fill = "Annual mean\nprecipitation, mm")+
theme_void()+ylim(-55,90)+
theme(legend.position = "right")
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 1, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = pr.mean))+
borders(colour = "grey60")+
binned_scale(aesthetics = "fill", breaks = p.breaks, palette = function(x) rev(colscale),
guide = guide_legend(label.theme = element_text(angle = 0)))+
labs(fill = "Annual mean\nprecipitation, mm", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom")
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
12.2.2 Precipitation anomalies
12.2.2.1 Gradient anomalies
cmip6s$pr.percent <- with(cmip6s, diff.pr.mean/spr.ref.mean*100)
quantile(cmip6s$pr.percent, probs = seq(0,1,0.1))
## 0% 10% 20% 30% 40% 50%
## -42.02999493 -2.87352237 0.00000000 0.04663897 2.46136429 5.19906939
## 60% 70% 80% 90% 100%
## 9.34748067 13.67239987 18.11758968 24.02085190 148.15226168
p.breaks <- c(-43,-3,0,3,5,9,13,18,24,148)
colscale <- c("#08519c", "#3182bd", "#6baed6", "#9ecae1", "#c6dbef", "#fddbc7", "#f4a582", "#d6604d", "#b2182b", "#67001f")map_list <- list()
for(i in c("1850_1880","1970_2000","1985_2015")){
g <- ggplot() + geom_raster(data = subset(cmip6s, period == i & model == "historical"), aes(x=lon, y = lat, fill = pr.percent))+
borders(colour = "grey60")+
scale_fill_gradient2(high = "#08519c", mid = "white", low = "#b2182b", midpoint = 0, transform = "pseudo_log", breaks = c(-10, -5, -1, 0, 1, 5, 10, 15, 30))+
labs(fill = "Precipitation anomalies in %", title = i)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom", legend.text = element_text(angle = 0))
map_list[[i]] <- g
}
ggpubr::ggarrange(plotlist = map_list, ncol = 1, nrow = 3, common.legend = T, legend = "bottom")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = pr.percent))+
borders(colour = "grey60")+
scale_fill_gradient2(high = "#08519c", mid = "white", low = "#b2182b", midpoint = 0, transform = "pseudo_log", breaks = c(-10, -5, -1, 0, 1, 5, 10, 15, 30))+
labs(fill = "Precipitation anomalies in %", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom", legend.text = element_text(angle = 0))
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
12.2.2.2 Binned anomalies
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot() + geom_raster(data = subset(cmip6s, period == j & model == i), aes(x=lon, y = lat, fill = pr.percent))+
borders(colour = "grey60")+
scale_fill_binned_divergingx(mid = 0, palette = "RdBu", rev = F, n_interp = 7, breaks = c(-20, -10, 0, 10, 20, 30, 40, 50, 60))+
labs(fill = "Precipitation anomalies in %", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom", legend.text = element_text(angle = 0))
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
BR <- brewer.pal(n = 11, name ="RdBu")
coltemp <- c("[-20,-10]" = BR[4], "(-10,0]" = BR[6], "(0,10]" = BR[6], "(10,20]" = BR[8], "(20,30]" = BR[9], "(30,40]" = BR[10], "(40,50]" = BR[11], "(50,60]" = "grey20")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot(data = subset(cmip6s, period == j & model == i),
aes(x=lon, y = lat, fill = cut(pr.percent, breaks = c(-20, -10, 0, 10, 20, 30, 40, 50, 60), include.lowest = T))) +
geom_tile(show.legend = T)+
borders(colour = "grey60")+
scale_fill_manual(values = coltemp, drop = F, na.translate = T)+
labs(fill = "Precipitation anomalies in %", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom", legend.text = element_text(angle = 0))+
guides(fill = guide_legend(nrow = 2))
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
BR <- brewer.pal(n = 11, name ="RdBu")
coltemp <- c("[-20,-10]" = BR[4], "(-10,0]" = BR[5], "(0,10]" = BR[7], "(10,20]" = BR[8], "(20,30]" = BR[9], "(30,40]" = BR[10], "(40,50]" = BR[11], "(50,60]" = "grey20")
map_list <- list()
for(i in c("SSP245","SSP370","SSP585")){
for (j in c("2030_2060","2070_2100")){
index <- paste(i, j, sep = " , ")
g <- ggplot(data = subset(cmip6s, period == j & model == i),
aes(x=lon, y = lat, fill = cut(pr.percent, breaks = c(-20, -10, 0, 10, 20, 30, 40, 50, 60), include.lowest = T))) +
geom_tile(show.legend = T)+
borders(colour = "grey60")+
scale_fill_manual(values = coltemp, drop = F, na.translate = T)+
labs(fill = "Precipitation anomalies in %", title = index)+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom", legend.text = element_text(angle = 0))+
guides(fill = guide_legend(nrow = 2))
map_list[[index]] <- g
}}
ggpubr::ggarrange(plotlist = map_list,ncol = 2, nrow = 3, common.legend = T, legend = "bottom")
13 Seasonnal aridity
In CMIP6, the precipitation (pr) and evapotranspiration (ET0) are in mm/day.
cmip6 <- read.table("cmip6_AI.txt")
cmip6$AI_01 <- with(cmip6, pr_01/ET0_01)
cmip6$AI_02 <- with(cmip6, pr_02/ET0_02)
cmip6$AI_03 <- with(cmip6, pr_03/ET0_03)
cmip6$AI_04 <- with(cmip6, pr_04/ET0_04)
cmip6$AI_05 <- with(cmip6, pr_05/ET0_05)
cmip6$AI_06 <- with(cmip6, pr_06/ET0_06)
cmip6$AI_07 <- with(cmip6, pr_07/ET0_07)
cmip6$AI_08 <- with(cmip6, pr_08/ET0_08)
cmip6$AI_09 <- with(cmip6, pr_09/ET0_09)
cmip6$AI_10 <- with(cmip6, pr_10/ET0_10)
cmip6$AI_11 <- with(cmip6, pr_11/ET0_11)
cmip6$AI_12 <- with(cmip6, pr_12/ET0_12)
cmip6.monthly.ref <- cmip6 %>% filter(period == "1970_2000") %>%
select(c("lon","lat","model","period","source", "Continent","Name",
"AI_01","pr_01","ET0_01",
"AI_02","pr_02","ET0_02",
"AI_03","pr_03","ET0_03",
"AI_04","pr_04","ET0_04",
"AI_05","pr_05","ET0_05",
"AI_06","pr_06","ET0_06",
"AI_07","pr_07","ET0_07",
"AI_08","pr_08","ET0_08",
"AI_09","pr_09","ET0_09",
"AI_10","pr_10","ET0_10",
"AI_11","pr_11","ET0_11",
"AI_12","pr_12","ET0_12"
)) %>%
setNames(c("lon","lat","model","period","source","Continent","Name",
"AI_01.ref","pr_01.ref","ET0_01.ref",
"AI_02.ref","pr_02.ref","ET0_02.ref",
"AI_03.ref","pr_03.ref","ET0_03.ref",
"AI_04.ref","pr_04.ref","ET0_04.ref",
"AI_05.ref","pr_05.ref","ET0_05.ref",
"AI_06.ref","pr_06.ref","ET0_06.ref",
"AI_07.ref","pr_07.ref","ET0_07.ref",
"AI_08.ref","pr_08.ref","ET0_08.ref",
"AI_09.ref","pr_09.ref","ET0_09.ref",
"AI_10.ref","pr_10.ref","ET0_10.ref",
"AI_11.ref","pr_11.ref","ET0_11.ref",
"AI_12.ref","pr_12.ref","ET0_12.ref"
))
cmip6monthly <- merge(
select(cmip6, c("lon","lat","source","model","period","source", "Continent","Name",
"AI_01","pr_01","ET0_01",
"AI_02","pr_02","ET0_02",
"AI_03","pr_03","ET0_03",
"AI_04","pr_04","ET0_04",
"AI_05","pr_05","ET0_05",
"AI_06","pr_06","ET0_06",
"AI_07","pr_07","ET0_07",
"AI_08","pr_08","ET0_08",
"AI_09","pr_09","ET0_09",
"AI_10","pr_10","ET0_10",
"AI_11","pr_11","ET0_11",
"AI_12","pr_12","ET0_12")),
select(cmip6.monthly.ref, c("lon","lat","source",
"AI_01.ref","pr_01.ref","ET0_01.ref",
"AI_02.ref","pr_02.ref","ET0_02.ref",
"AI_03.ref","pr_03.ref","ET0_03.ref",
"AI_04.ref","pr_04.ref","ET0_04.ref",
"AI_05.ref","pr_05.ref","ET0_05.ref",
"AI_06.ref","pr_06.ref","ET0_06.ref",
"AI_07.ref","pr_07.ref","ET0_07.ref",
"AI_08.ref","pr_08.ref","ET0_08.ref",
"AI_09.ref","pr_09.ref","ET0_09.ref",
"AI_10.ref","pr_10.ref","ET0_10.ref",
"AI_11.ref","pr_11.ref","ET0_11.ref",
"AI_12.ref","pr_12.ref","ET0_12.ref")),
by = c("lon","lat","source"), all.x = T)
write.table(cmip6monthly,"cmip6monthly.txt")cmip6monthly <- read.table("cmip6monthly.txt")
cmip6smonthly <- cmip6monthly %>%
group_by(lon,lat, Continent, Name, model, period) %>%
dplyr::summarise(AI_01_mean = mean(AI_01, na.rm = T), AI_01_sd = sd(AI_01, na.rm = T),
AI_02_mean = mean(AI_02, na.rm = T), AI_02_sd = sd(AI_02, na.rm = T),
AI_03_mean = mean(AI_03, na.rm = T), AI_03_sd = sd(AI_03, na.rm = T),
AI_04_mean = mean(AI_04, na.rm = T), AI_04_sd = sd(AI_04, na.rm = T),
AI_05_mean = mean(AI_05, na.rm = T), AI_05_sd = sd(AI_05, na.rm = T),
AI_06_mean = mean(AI_06, na.rm = T), AI_06_sd = sd(AI_06, na.rm = T),
AI_07_mean = mean(AI_07, na.rm = T), AI_07_sd = sd(AI_07, na.rm = T),
AI_08_mean = mean(AI_08, na.rm = T), AI_08_sd = sd(AI_08, na.rm = T),
AI_09_mean = mean(AI_09, na.rm = T), AI_09_sd = sd(AI_09, na.rm = T),
AI_10_mean = mean(AI_10, na.rm = T), AI_10_sd = sd(AI_10, na.rm = T),
AI_11_mean = mean(AI_11, na.rm = T), AI_11_sd = sd(AI_11, na.rm = T),
AI_12_mean = mean(AI_12, na.rm = T), AI_11_sd = sd(AI_12, na.rm = T),
ET0_01_mean = mean(ET0_01*31, na.rm = T), ET0_01_sd = sd(ET0_01*31, na.rm = T),
ET0_02_mean = mean(ET0_02*38, na.rm = T), ET0_02_sd = sd(ET0_02*28, na.rm = T),
ET0_03_mean = mean(ET0_03*31, na.rm = T), ET0_03_sd = sd(ET0_03*31, na.rm = T),
ET0_04_mean = mean(ET0_04*30, na.rm = T), ET0_04_sd = sd(ET0_04*30, na.rm = T),
ET0_05_mean = mean(ET0_05*31, na.rm = T), ET0_05_sd = sd(ET0_05*31, na.rm = T),
ET0_06_mean = mean(ET0_06*30, na.rm = T), ET0_06_sd = sd(ET0_06*30, na.rm = T),
ET0_07_mean = mean(ET0_07*31, na.rm = T), ET0_07_sd = sd(ET0_07*31, na.rm = T),
ET0_08_mean = mean(ET0_08*31, na.rm = T), ET0_08_sd = sd(ET0_08*31, na.rm = T),
ET0_09_mean = mean(ET0_09*30, na.rm = T), ET0_09_sd = sd(ET0_09*30, na.rm = T),
ET0_10_mean = mean(ET0_10*31, na.rm = T), ET0_10_sd = sd(ET0_10*31, na.rm = T),
ET0_11_mean = mean(ET0_11*30, na.rm = T), ET0_11_sd = sd(ET0_11*30, na.rm = T),
ET0_12_mean = mean(ET0_12*31, na.rm = T), ET0_11_sd = sd(ET0_12*31, na.rm = T),
pr_01_mean = mean(pr_01*31, na.rm = T), pr_01_sd = sd(pr_01*31, na.rm = T),
pr_02_mean = mean(pr_02*28, na.rm = T), pr_02_sd = sd(pr_02*28, na.rm = T),
pr_03_mean = mean(pr_03*31, na.rm = T), pr_03_sd = sd(pr_03*31, na.rm = T),
pr_04_mean = mean(pr_04*30, na.rm = T), pr_04_sd = sd(pr_04*30, na.rm = T),
pr_05_mean = mean(pr_05*31, na.rm = T), pr_05_sd = sd(pr_05*31, na.rm = T),
pr_06_mean = mean(pr_06*30, na.rm = T), pr_06_sd = sd(pr_06*30, na.rm = T),
pr_07_mean = mean(pr_07*31, na.rm = T), pr_07_sd = sd(pr_07*31, na.rm = T),
pr_08_mean = mean(pr_08*31, na.rm = T), pr_08_sd = sd(pr_08*31, na.rm = T),
pr_09_mean = mean(pr_09*30, na.rm = T), pr_09_sd = sd(pr_09*30, na.rm = T),
pr_10_mean = mean(pr_10*31, na.rm = T), pr_10_sd = sd(pr_10*31, na.rm = T),
pr_11_mean = mean(pr_11*30, na.rm = T), pr_11_sd = sd(pr_11*30, na.rm = T),
pr_12_mean = mean(pr_12*31, na.rm = T), pr_11_sd = sd(pr_12*31, na.rm = T)
) %>%
ungroup() %>% as.data.frame()
cmip6smonthly.ref <- cmip6smonthly %>% filter(period == "1970_2000") %>%
select(c("lon","lat","model","period", "Continent","Name",
"AI_01_mean","ET0_01_mean","pr_01_mean",
"AI_02_mean","ET0_02_mean","pr_02_mean",
"AI_03_mean","ET0_03_mean","pr_03_mean",
"AI_04_mean","ET0_04_mean","pr_04_mean",
"AI_05_mean","ET0_05_mean","pr_05_mean",
"AI_06_mean","ET0_06_mean","pr_06_mean",
"AI_07_mean","ET0_07_mean","pr_07_mean",
"AI_08_mean","ET0_08_mean","pr_08_mean",
"AI_09_mean","ET0_09_mean","pr_09_mean",
"AI_10_mean","ET0_10_mean","pr_10_mean",
"AI_11_mean","ET0_11_mean","pr_11_mean",
"AI_12_mean","ET0_12_mean","pr_12_mean",
)) %>%
setNames(c("lon","lat","model","period","Continent","Name",
"AI_01_mean.ref","ET0_01_mean.ref","pr_01_mean.ref",
"AI_02_mean.ref","ET0_02_mean.ref","pr_02_mean.ref",
"AI_03_mean.ref","ET0_03_mean.ref","pr_03_mean.ref",
"AI_04_mean.ref","ET0_04_mean.ref","pr_04_mean.ref",
"AI_05_mean.ref","ET0_05_mean.ref","pr_05_mean.ref",
"AI_06_mean.ref","ET0_06_mean.ref","pr_06_mean.ref",
"AI_07_mean.ref","ET0_07_mean.ref","pr_07_mean.ref",
"AI_08_mean.ref","ET0_08_mean.ref","pr_08_mean.ref",
"AI_09_mean.ref","ET0_09_mean.ref","pr_09_mean.ref",
"AI_10_mean.ref","ET0_10_mean.ref","pr_10_mean.ref",
"AI_11_mean.ref","ET0_11_mean.ref","pr_11_mean.ref",
"AI_12_mean.ref","ET0_12_mean.ref","pr_12_mean.ref"
))
cmip6smonthly <- merge(cmip6smonthly,cmip6smonthly.ref, by = c("lon","lat","model","period", "Continent","Name"), all.x = T)
# CATEGORIES 01
cmip6smonthly$cat.AI_01 <- cmip6smonthly$AI_01_mean %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_01[which(cmip6smonthly$ET0_01_mean < 400/12)] <- "Cold"
cmip6smonthly$cat.AI_01.ref <- cmip6smonthly$AI_01_mean.ref %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_01.ref[which(cmip6smonthly$ET0_01_mean.ref < 400/12)] <- "Cold"
# CATEGORIES 02
cmip6smonthly$cat.AI_02 <- cmip6smonthly$AI_02_mean %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_02[which(cmip6smonthly$ET0_02_mean < 400/12)] <- "Cold"
cmip6smonthly$cat.AI_02.ref <- cmip6smonthly$AI_02_mean.ref %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_02.ref[which(cmip6smonthly$ET0_02_mean.ref < 400/12)] <- "Cold"
# CATEGORIES 03
cmip6smonthly$cat.AI_03 <- cmip6smonthly$AI_03_mean %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_03[which(cmip6smonthly$ET0_03_mean < 400/12)] <- "Cold"
cmip6smonthly$cat.AI_03.ref <- cmip6smonthly$AI_03_mean.ref %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_03.ref[which(cmip6smonthly$ET0_03_mean.ref < 400/12)] <- "Cold"
# CATEGORIES 04
cmip6smonthly$cat.AI_04 <- cmip6smonthly$AI_04_mean %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_04[which(cmip6smonthly$ET0_04_mean < 400/12)] <- "Cold"
cmip6smonthly$cat.AI_04.ref <- cmip6smonthly$AI_05_mean.ref %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_04.ref[which(cmip6smonthly$ET0_05_mean.ref < 400/12)] <- "Cold"
# CATEGORIES 05
cmip6smonthly$cat.AI_05 <- cmip6smonthly$AI_05_mean %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_05[which(cmip6smonthly$ET0_05_mean < 400/12)] <- "Cold"
cmip6smonthly$cat.AI_05.ref <- cmip6smonthly$AI_05_mean.ref %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_05.ref[which(cmip6smonthly$ET0_05_mean.ref < 400/12)] <- "Cold"
# CATEGORIES 06
cmip6smonthly$cat.AI_06 <- cmip6smonthly$AI_06_mean %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_06[which(cmip6smonthly$ET0_06_mean < 400/12)] <- "Cold"
cmip6smonthly$cat.AI_06.ref <- cmip6smonthly$AI_06_mean.ref %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_06.ref[which(cmip6smonthly$ET0_06_mean.ref < 400/12)] <- "Cold"
# CATEGORIES 07
cmip6smonthly$cat.AI_07 <- cmip6smonthly$AI_07_mean %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_07[which(cmip6smonthly$ET0_07_mean < 400/12)] <- "Cold"
cmip6smonthly$cat.AI_07.ref <- cmip6smonthly$AI_07_mean.ref %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_07.ref[which(cmip6smonthly$ET0_07_mean.ref < 400/12)] <- "Cold"
# CATEGORIES 08
cmip6smonthly$cat.AI_08 <- cmip6smonthly$AI_08_mean %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_08[which(cmip6smonthly$ET0_08_mean < 400/12)] <- "Cold"
cmip6smonthly$cat.AI_08.ref <- cmip6smonthly$AI_08_mean.ref %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_08.ref[which(cmip6smonthly$ET0_08_mean.ref < 400/12)] <- "Cold"
# CATEGORIES 09
cmip6smonthly$cat.AI_09 <- cmip6smonthly$AI_09_mean %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_09[which(cmip6smonthly$ET0_09_mean < 400/12)] <- "Cold"
cmip6smonthly$cat.AI_09.ref <- cmip6smonthly$AI_09_mean.ref %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_09.ref[which(cmip6smonthly$ET0_09_mean.ref < 400/12)] <- "Cold"
# CATEGORIES 10
cmip6smonthly$cat.AI_10 <- cmip6smonthly$AI_10_mean %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_10[which(cmip6smonthly$ET0_10_mean < 400/12)] <- "Cold"
cmip6smonthly$cat.AI_10.ref <- cmip6smonthly$AI_10_mean.ref %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_10.ref[which(cmip6smonthly$ET0_10_mean.ref < 400/12)] <- "Cold"
# CATEGORIES 11
cmip6smonthly$cat.AI_11 <- cmip6smonthly$AI_11_mean %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_11[which(cmip6smonthly$ET0_11_mean < 400/12)] <- "Cold"
cmip6smonthly$cat.AI_11.ref <- cmip6smonthly$AI_11_mean.ref %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_11.ref[which(cmip6smonthly$ET0_11_mean.ref < 400/12)] <- "Cold"
# CATEGORIES 12
cmip6smonthly$cat.AI_12 <- cmip6smonthly$AI_12_mean %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_12[which(cmip6smonthly$ET0_12_mean < 400/12)] <- "Cold"
cmip6smonthly$cat.AI_12.ref <- cmip6smonthly$AI_12_mean.ref %>% cut(breaks = breaks.unesco) %>% revalue(cat.unesco)
cmip6smonthly$cat.AI_12.ref[which(cmip6smonthly$ET0_12_mean.ref < 400/12)] <- "Cold"
write.table(cmip6smonthly, "cmip6smonthly.txt")cmip6smonthly <- read.table("cmip6smonthly.txt")
cmip6smonthly$cat.AI_01 <- factor(cmip6smonthly$cat.AI_01, levels = c("Hyperarid","Arid","Semi-arid","Dry subhumid","Humid","Cold"))
ggarrange(
ggplot() +
geom_tile(data = subset(cmip6smonthly, period == "1970_2000" & model == "historical"), aes(x=lon, y = lat, fill = cat.AI_01))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = "January", fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom"),
ggplot() +
geom_tile(data = subset(cmip6smonthly, period == "1970_2000" & model == "historical"), aes(x=lon, y = lat, fill = cat.AI_02))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = "February", fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom"),
ggplot() +
geom_tile(data = subset(cmip6smonthly, period == "1970_2000" & model == "historical"), aes(x=lon, y = lat, fill = cat.AI_03))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = "March", fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom"),
ggplot() +
geom_tile(data = subset(cmip6smonthly, period == "1970_2000" & model == "historical"), aes(x=lon, y = lat, fill = cat.AI_04))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = "April", fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom"),
ggplot() +
geom_tile(data = subset(cmip6smonthly, period == "1970_2000" & model == "historical"), aes(x=lon, y = lat, fill = cat.AI_05))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = "May", fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom"),
ggplot() +
geom_tile(data = subset(cmip6smonthly, period == "1970_2000" & model == "historical"), aes(x=lon, y = lat, fill = cat.AI_06))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = "June", fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom"),
ggplot() +
geom_tile(data = subset(cmip6smonthly, period == "1970_2000" & model == "historical"), aes(x=lon, y = lat, fill = cat.AI_07))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = "July", fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom"),
ggplot() +
geom_tile(data = subset(cmip6smonthly, period == "1970_2000" & model == "historical"), aes(x=lon, y = lat, fill = cat.AI_08))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = "August", fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom"),
ggplot() +
geom_tile(data = subset(cmip6smonthly, period == "1970_2000" & model == "historical"), aes(x=lon, y = lat, fill = cat.AI_09))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = "September", fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom"),
ggplot() +
geom_tile(data = subset(cmip6smonthly, period == "1970_2000" & model == "historical"), aes(x=lon, y = lat, fill = cat.AI_10))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = "October", fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom"),
ggplot() +
geom_tile(data = subset(cmip6smonthly, period == "1970_2000" & model == "historical"), aes(x=lon, y = lat, fill = cat.AI_11))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = "November", fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom"),
ggplot() +
geom_tile(data = subset(cmip6smonthly, period == "1970_2000" & model == "historical"), aes(x=lon, y = lat, fill = cat.AI_12))+
borders(colour = "grey60")+
scale_fill_manual(values = col.cat, na.translate = F)+
labs(title = "December", fill = "")+
theme_void()+ylim(-55,90)+
theme(legend.position = "bottom"),
ncol = 3, nrow = 4, common.legend = T, legend = "bottom"
)
13.1 Evolution of monthly aridity by continent
cmip6smm <- cmip6smonthly %>% group_by(model, period, Continent) %>%
summarise(AI_01 = median(AI_01_mean, na.rm = T), cov_AI_01 = sd(AI_01_mean, na.rm = T)/mean(AI_01_mean, na.rm = T),
AI_02 = median(AI_02_mean, na.rm = T), cov_AI_02 = sd(AI_02_mean, na.rm = T)/mean(AI_02_mean, na.rm = T),
AI_03 = median(AI_03_mean, na.rm = T), cov_AI_03 = sd(AI_03_mean, na.rm = T)/mean(AI_03_mean, na.rm = T),
AI_04 = median(AI_04_mean, na.rm = T), cov_AI_04 = sd(AI_04_mean, na.rm = T)/mean(AI_04_mean, na.rm = T),
AI_05 = median(AI_05_mean, na.rm = T), cov_AI_05 = sd(AI_05_mean, na.rm = T)/mean(AI_05_mean, na.rm = T),
AI_06 = median(AI_06_mean, na.rm = T), cov_AI_06 = sd(AI_06_mean, na.rm = T)/mean(AI_06_mean, na.rm = T),
AI_07 = median(AI_07_mean, na.rm = T), cov_AI_07 = sd(AI_07_mean, na.rm = T)/mean(AI_07_mean, na.rm = T),
AI_08 = median(AI_08_mean, na.rm = T), cov_AI_08 = sd(AI_08_mean, na.rm = T)/mean(AI_08_mean, na.rm = T),
AI_09 = median(AI_09_mean, na.rm = T), cov_AI_09 = sd(AI_09_mean, na.rm = T)/mean(AI_09_mean, na.rm = T),
AI_10 = median(AI_10_mean, na.rm = T), cov_AI_10 = sd(AI_10_mean, na.rm = T)/mean(AI_10_mean, na.rm = T),
AI_11 = median(AI_11_mean, na.rm = T), cov_AI_11 = sd(AI_11_mean, na.rm = T)/mean(AI_11_mean, na.rm = T),
AI_12 = median(AI_12_mean, na.rm = T), cov_AI_12 = sd(AI_12_mean, na.rm = T)/mean(AI_12_mean, na.rm = T)) %>%
ungroup() %>% as.data.frame()
# cmip6smm <- cmip6smonthly %>% group_by(model, period, Continent) %>%
# summarise(AI_01 = mean(AI_01_mean, na.rm = T),
# AI_02 = mean(AI_02_mean, na.rm = T),
# AI_03 = mean(AI_03_mean, na.rm = T),
# AI_04 = mean(AI_04_mean, na.rm = T),
# AI_05 = mean(AI_05_mean, na.rm = T),
# AI_06 = mean(AI_06_mean, na.rm = T),
# AI_07 = mean(AI_07_mean, na.rm = T),
# AI_08 = mean(AI_08_mean, na.rm = T),
# AI_09 = mean(AI_09_mean, na.rm = T),
# AI_10 = mean(AI_10_mean, na.rm = T),
# AI_11 = mean(AI_11_mean, na.rm = T),
# AI_12 = mean(AI_12_mean, na.rm = T)) %>%
# ungroup() %>% as.data.frame()
long_AI <- cmip6smm %>% select(c("model","period","Continent","AI_01","AI_02","AI_03","AI_04","AI_05","AI_06","AI_07","AI_08","AI_09","AI_10","AI_11","AI_12")) %>%
tidyr::pivot_longer(
cols = starts_with("AI_"),
names_to= "Month",
values_to = "AI")
long_AI$Month <- revalue(long_AI$Month, c("AI_01" = "January","AI_02" = "February", "AI_03" = "March", "AI_04" = "April","AI_05" ="May","AI_06" = "June", "AI_07" ="July","AI_08" = "August","AI_09" = "September","AI_10" = "October","AI_11" = "November","AI_12" = "December"))
long_sdAI <- cmip6smm %>%
select(c("model","period","Continent","cov_AI_01","cov_AI_02","cov_AI_03","cov_AI_04","cov_AI_05","cov_AI_06","cov_AI_07","cov_AI_08","cov_AI_09","cov_AI_10","cov_AI_11","cov_AI_12")) %>%
tidyr::pivot_longer(
cols = starts_with("cov_AI_"),
names_to= "Month",
values_to = "cov_AI"
)
long_sdAI$Month <- revalue(long_sdAI$Month, c("cov_AI_01" = "January","cov_AI_02" = "February", "cov_AI_03" = "March", "cov_AI_04" = "April","cov_AI_05" ="May","cov_AI_06" = "June", "cov_AI_07" ="July","cov_AI_08" = "August","cov_AI_09" = "September","cov_AI_10" = "October","cov_AI_11" = "November","cov_AI_12" = "December"))
cmip6sml <- merge(long_AI,long_sdAI, by = c("model","period","Continent","Month"))
cmip6sml$Month <- factor(cmip6sml$Month, c("January","February","March","April","May","June","July","August","September","October","November","December"))
col.model <- c("historical" = "#4575B4", "SSP245" = "#FEE08B", "SSP370" = "#FDAE61", "SSP585" = "#D73027")
cmip6sml$NMonth <- cmip6sml$Month %>% revalue(c("January" = 1, "February" =2, "March"= 3, "April"= 4,"May" = 5, "June" = 6,"July" = 7, "August"= 8,"September" = 9, "October" = 10,"November" = 11, "December" = 12))
for(i in c("NORTH-AMERICA","CENTRAL-AMERICA","SOUTH-AMERICA","EUROPE","EUROPE-AFRICA","AFRICA","ASIA","OCEANIA")){
g <- ggplot(subset(cmip6sml, Continent == i & period %in% c("1970_2000","2070_2100")))+
# geom_rect(aes(xmin = 1, xmax = 12, ymin= 0, ymax = 0.03), fill = "#D73027", alpha = 0.02)+
# geom_rect(aes(xmin = 1, xmax = 12, ymin= 0.03, ymax = 0.2), fill = "#FDAE61", alpha = 0.02)+
# geom_rect(aes(xmin = 1, xmax = 12, ymin= 0.2, ymax = 0.5), fill = "#FEE08B", alpha = 0.02)+
# geom_rect(aes(xmin = 1, xmax = 12, ymin= 0.5, ymax = 0.65), fill = "#ABD9E9", alpha = 0.02)+
# geom_rect(aes(xmin = 1, xmax = 12, ymin= 0.65, ymax = Inf), fill = "#4575B4", alpha = 0.02)+
geom_point(aes(x=Month, y = AI, size = cov_AI, col = model), alpha = 0.5)+
geom_line(aes(x=Month, y = AI, col = model, group = model, linetype = model), alpha = 0.5)+
scale_color_manual(values = col.model)+
scale_size_continuous(transform = "pseudo_log")+
labs(y = "Median aridity index", size = "Coefficient of variance", col = "Model", linetype = "Model", title = i)+
theme_minimal()+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
print(g)
}
13.2 Evolution of monthly evapotranspiration by continent
cmip6smm_et0 <- cmip6smonthly %>% group_by(model, period, Continent) %>%
summarise(ET0_01 = median(ET0_01_mean, na.rm = T), cov_ET0_01 = sd(ET0_01_mean, na.rm = T)/mean(ET0_01_mean, na.rm = T),
ET0_02 = median(ET0_02_mean, na.rm = T), cov_ET0_02 = sd(ET0_02_mean, na.rm = T)/mean(ET0_02_mean, na.rm = T),
ET0_03 = median(ET0_03_mean, na.rm = T), cov_ET0_03 = sd(ET0_03_mean, na.rm = T)/mean(ET0_03_mean, na.rm = T),
ET0_04 = median(ET0_04_mean, na.rm = T), cov_ET0_04 = sd(ET0_04_mean, na.rm = T)/mean(ET0_04_mean, na.rm = T),
ET0_05 = median(ET0_05_mean, na.rm = T), cov_ET0_05 = sd(ET0_05_mean, na.rm = T)/mean(ET0_05_mean, na.rm = T),
ET0_06 = median(ET0_06_mean, na.rm = T), cov_ET0_06 = sd(ET0_06_mean, na.rm = T)/mean(ET0_06_mean, na.rm = T),
ET0_07 = median(ET0_07_mean, na.rm = T), cov_ET0_07 = sd(ET0_07_mean, na.rm = T)/mean(ET0_07_mean, na.rm = T),
ET0_08 = median(ET0_08_mean, na.rm = T), cov_ET0_08 = sd(ET0_08_mean, na.rm = T)/mean(ET0_08_mean, na.rm = T),
ET0_09 = median(ET0_09_mean, na.rm = T), cov_ET0_09 = sd(ET0_09_mean, na.rm = T)/mean(ET0_09_mean, na.rm = T),
ET0_10 = median(ET0_10_mean, na.rm = T), cov_ET0_10 = sd(ET0_10_mean, na.rm = T)/mean(ET0_10_mean, na.rm = T),
ET0_11 = median(ET0_11_mean, na.rm = T), cov_ET0_11 = sd(ET0_11_mean, na.rm = T)/mean(ET0_11_mean, na.rm = T),
ET0_12 = median(ET0_12_mean, na.rm = T), cov_ET0_12 = sd(ET0_12_mean, na.rm = T)/mean(ET0_12_mean, na.rm = T)) %>%
ungroup() %>% as.data.frame()
# cmip6smm <- cmip6smonthly %>% group_by(model, period, Continent) %>%
# summarise(ET0_01 = mean(ET0_01_mean, na.rm = T),
# ET0_02 = mean(ET0_02_mean, na.rm = T),
# ET0_03 = mean(ET0_03_mean, na.rm = T),
# ET0_04 = mean(ET0_04_mean, na.rm = T),
# ET0_05 = mean(ET0_05_mean, na.rm = T),
# ET0_06 = mean(ET0_06_mean, na.rm = T),
# ET0_07 = mean(ET0_07_mean, na.rm = T),
# ET0_08 = mean(ET0_08_mean, na.rm = T),
# ET0_09 = mean(ET0_09_mean, na.rm = T),
# ET0_10 = mean(ET0_10_mean, na.rm = T),
# ET0_11 = mean(ET0_11_mean, na.rm = T),
# ET0_12 = mean(ET0_12_mean, na.rm = T)) %>%
# ungroup() %>% as.data.frame()
long_ET0 <- cmip6smm_et0 %>% select(c("model","period","Continent","ET0_01","ET0_02","ET0_03","ET0_04","ET0_05","ET0_06","ET0_07","ET0_08","ET0_09","ET0_10","ET0_11","ET0_12")) %>%
tidyr::pivot_longer(
cols = starts_with("ET0_"),
names_to= "Month",
values_to = "ET0")
long_ET0$Month <- revalue(long_AI$Month, c("ET0_01" = "January","ET0_02" = "February", "ET0_03" = "March", "ET0_04" = "April","ET0_05" ="May","ET0_06" = "June", "ET0_07" ="July","ET0_08" = "August","ET0_09" = "September","ET0_10" = "October","ET0_11" = "November","ET0_12" = "December"))
long_sdET0 <- cmip6smm_et0 %>%
select(c("model","period","Continent","cov_ET0_01","cov_ET0_02","cov_ET0_03","cov_ET0_04","cov_ET0_05","cov_ET0_06","cov_ET0_07","cov_ET0_08","cov_ET0_09","cov_ET0_10","cov_ET0_11","cov_ET0_12")) %>%
tidyr::pivot_longer(
cols = starts_with("cov_ET0_"),
names_to= "Month",
values_to = "cov_ET0"
)
long_sdET0$Month <- revalue(long_sdET0$Month, c("cov_ET0_01" = "January","cov_ET0_02" = "February", "cov_ET0_03" = "March", "cov_ET0_04" = "April","cov_ET0_05" ="May","cov_ET0_06" = "June", "cov_ET0_07" ="July","cov_ET0_08" = "August","cov_ET0_09" = "September","cov_ET0_10" = "October","cov_ET0_11" = "November","cov_ET0_12" = "December"))
cmip6sml_et0 <- merge(long_ET0,long_sdET0, by = c("model","period","Continent","Month"))
cmip6sml_et0$Month <- factor(cmip6sml_et0$Month, c("January","February","March","April","May","June","July","August","September","October","November","December"))
col.model <- c("historical" = "#4575B4", "SSP245" = "#FEE08B", "SSP370" = "#FDAE61", "SSP585" = "#D73027")
cmip6sml_et0$NMonth <- cmip6sml_et0$Month %>% revalue(c("January" = 1, "February" =2, "March"= 3, "April"= 4,"May" = 5, "June" = 6,"July" = 7, "August"= 8,"September" = 9, "October" = 10,"November" = 11, "December" = 12))
for(i in c("NORTH-AMERICA","CENTRAL-AMERICA","SOUTH-AMERICA","EUROPE","EUROPE-AFRICA","AFRICA","ASIA","OCEANIA")){
g <- ggplot(subset(cmip6sml_et0, Continent == i & period %in% c("1970_2000","2070_2100")))+
# geom_rect(aes(xmin = 1, xmax = 12, ymin= 0, ymax = 0.03), fill = "#D73027", alpha = 0.02)+
# geom_rect(aes(xmin = 1, xmax = 12, ymin= 0.03, ymax = 0.2), fill = "#FDAE61", alpha = 0.02)+
# geom_rect(aes(xmin = 1, xmax = 12, ymin= 0.2, ymax = 0.5), fill = "#FEE08B", alpha = 0.02)+
# geom_rect(aes(xmin = 1, xmax = 12, ymin= 0.5, ymax = 0.65), fill = "#ABD9E9", alpha = 0.02)+
# geom_rect(aes(xmin = 1, xmax = 12, ymin= 0.65, ymax = Inf), fill = "#4575B4", alpha = 0.02)+
geom_point(aes(x=Month, y = ET0, size = cov_ET0, col = model), alpha = 0.5)+
geom_line(aes(x=Month, y = ET0, col = model, group = model, linetype = model), alpha = 0.5)+
scale_color_manual(values = col.model)+
scale_size_continuous(transform = "pseudo_log")+
labs(y = "Median monthly evapotranspiration, mm", size = "Coefficient of variance", col = "Model", linetype = "Model", title = i)+
theme_minimal()+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
print(g)
}
13.3 Evolution of monthly precipitation by continent
cmip6smm_pr <- cmip6smonthly %>% group_by(model, period, Continent) %>%
summarise(pr_01 = median(pr_01_mean, na.rm = T), cov_pr_01 = sd(pr_01_mean, na.rm = T)/mean(pr_01_mean, na.rm = T),
pr_02 = median(pr_02_mean, na.rm = T), cov_pr_02 = sd(pr_02_mean, na.rm = T)/mean(pr_02_mean, na.rm = T),
pr_03 = median(pr_03_mean, na.rm = T), cov_pr_03 = sd(pr_03_mean, na.rm = T)/mean(pr_03_mean, na.rm = T),
pr_04 = median(pr_04_mean, na.rm = T), cov_pr_04 = sd(pr_04_mean, na.rm = T)/mean(pr_04_mean, na.rm = T),
pr_05 = median(pr_05_mean, na.rm = T), cov_pr_05 = sd(pr_05_mean, na.rm = T)/mean(pr_05_mean, na.rm = T),
pr_06 = median(pr_06_mean, na.rm = T), cov_pr_06 = sd(pr_06_mean, na.rm = T)/mean(pr_06_mean, na.rm = T),
pr_07 = median(pr_07_mean, na.rm = T), cov_pr_07 = sd(pr_07_mean, na.rm = T)/mean(pr_07_mean, na.rm = T),
pr_08 = median(pr_08_mean, na.rm = T), cov_pr_08 = sd(pr_08_mean, na.rm = T)/mean(pr_08_mean, na.rm = T),
pr_09 = median(pr_09_mean, na.rm = T), cov_pr_09 = sd(pr_09_mean, na.rm = T)/mean(pr_09_mean, na.rm = T),
pr_10 = median(pr_10_mean, na.rm = T), cov_pr_10 = sd(pr_10_mean, na.rm = T)/mean(pr_10_mean, na.rm = T),
pr_11 = median(pr_11_mean, na.rm = T), cov_pr_11 = sd(pr_11_mean, na.rm = T)/mean(pr_11_mean, na.rm = T),
pr_12 = median(pr_12_mean, na.rm = T), cov_pr_12 = sd(pr_12_mean, na.rm = T)/mean(pr_12_mean, na.rm = T)) %>%
ungroup() %>% as.data.frame()
# cmip6smm <- cmip6smonthly %>% group_by(model, period, Continent) %>%
# summarise(pr_01 = mean(pr_01_mean, na.rm = T),
# pr_02 = mean(pr_02_mean, na.rm = T),
# pr_03 = mean(pr_03_mean, na.rm = T),
# pr_04 = mean(pr_04_mean, na.rm = T),
# pr_05 = mean(pr_05_mean, na.rm = T),
# pr_06 = mean(pr_06_mean, na.rm = T),
# pr_07 = mean(pr_07_mean, na.rm = T),
# pr_08 = mean(pr_08_mean, na.rm = T),
# pr_09 = mean(pr_09_mean, na.rm = T),
# pr_10 = mean(pr_10_mean, na.rm = T),
# pr_11 = mean(pr_11_mean, na.rm = T),
# pr_12 = mean(pr_12_mean, na.rm = T)) %>%
# ungroup() %>% as.data.frame()
long_pr <- cmip6smm_pr %>% select(c("model","period","Continent","pr_01","pr_02","pr_03","pr_04","pr_05","pr_06","pr_07","pr_08","pr_09","pr_10","pr_11","pr_12")) %>%
tidyr::pivot_longer(
cols = starts_with("pr_"),
names_to= "Month",
values_to = "pr")
long_pr$Month <- revalue(long_AI$Month, c("pr_01" = "January","pr_02" = "February", "pr_03" = "March", "pr_04" = "Apr_il","pr_05" ="May","pr_06" = "June", "pr_07" ="July","pr_08" = "August","pr_09" = "September","pr_10" = "October","pr_11" = "November","pr_12" = "December"))
long_sdpr <- cmip6smm_pr %>%
select(c("model","period","Continent","cov_pr_01","cov_pr_02","cov_pr_03","cov_pr_04","cov_pr_05","cov_pr_06","cov_pr_07","cov_pr_08","cov_pr_09","cov_pr_10","cov_pr_11","cov_pr_12")) %>%
tidyr::pivot_longer(
cols = starts_with("cov_pr_"),
names_to= "Month",
values_to = "cov_pr"
)
long_sdpr$Month <- revalue(long_pr$Month, c("cov_pr_01" = "January","cov_pr_02" = "February", "cov_pr_03" = "March", "cov_pr_04" = "April","cov_pr_05" ="May","cov_pr_06" = "June", "cov_pr_07" ="July","cov_pr_08" = "August","cov_pr_09" = "September","cov_pr_10" = "October","cov_pr_11" = "November","cov_pr_12" = "December"))
cmip6sml_pr <- merge(long_pr,long_sdpr, by = c("model","period","Continent","Month"))
cmip6sml_pr$Month <- factor(cmip6sml_pr$Month, c("January","February","March","April","May","June","July","August","September","October","November","December"))
col.model <- c("historical" = "#4575B4", "SSP245" = "#FEE08B", "SSP370" = "#FDAE61", "SSP585" = "#D73027")
cmip6sml_pr$NMonth <- cmip6sml_pr$Month %>% revalue(c("January" = 1, "February" =2, "March"= 3, "April"= 4,"May" = 5, "June" = 6,"July" = 7, "August"= 8,"September" = 9, "October" = 10,"November" = 11, "December" = 12))
for(i in c("NORTH-AMERICA","CENTRAL-AMERICA","SOUTH-AMERICA","EUROPE","EUROPE-AFRICA","AFRICA","ASIA","OCEANIA")){
g <- ggplot(subset(cmip6sml_pr, Continent == i & period %in% c("1970_2000","2070_2100")))+
# geom_rect(aes(xmin = 1, xmax = 12, ymin= 0, ymax = 0.03), fill = "#D73027", alpha = 0.02)+
# geom_rect(aes(xmin = 1, xmax = 12, ymin= 0.03, ymax = 0.2), fill = "#FDAE61", alpha = 0.02)+
# geom_rect(aes(xmin = 1, xmax = 12, ymin= 0.2, ymax = 0.5), fill = "#FEE08B", alpha = 0.02)+
# geom_rect(aes(xmin = 1, xmax = 12, ymin= 0.5, ymax = 0.65), fill = "#ABD9E9", alpha = 0.02)+
# geom_rect(aes(xmin = 1, xmax = 12, ymin= 0.65, ymax = Inf), fill = "#4575B4", alpha = 0.02)+
geom_point(aes(x=Month, y = pr, size = cov_pr, col = model), alpha = 0.5)+
geom_line(aes(x=Month, y = pr, col = model, group = model, linetype = model), alpha = 0.5)+
scale_color_manual(values = col.model)+
scale_size_continuous(transform = "pseudo_log")+
labs(y = "Median monthly precipitation, mm", size = "Coefficient of variance", col = "Model", linetype = "Model", title = i)+
theme_minimal()+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
print(g)
}
